├── runtime.txt ├── Procfile ├── Demos ├── iris.gif ├── loan.gif ├── main.png ├── boston.gif ├── gender.gif └── weight.gif ├── static ├── files │ └── Resume.pdf ├── images │ ├── icons │ │ ├── a.ico │ │ ├── ml.png │ │ ├── www.png │ │ ├── blog.png │ │ ├── mail.png │ │ ├── phone.png │ │ ├── github.png │ │ ├── kaggle.png │ │ ├── location.png │ │ ├── twitter.png │ │ ├── whatsapp.png │ │ ├── youtube.png │ │ ├── instagram.png │ │ ├── linked-in.png │ │ └── a.svg │ └── photos │ │ ├── ask.jpeg │ │ ├── iris.jpg │ │ ├── flask.png │ │ ├── fork_it.png │ │ ├── gender.png │ │ ├── house.jpg │ │ ├── numpy.png │ │ ├── pandas.png │ │ ├── python.jpeg │ │ ├── python.png │ │ ├── seaborn.png │ │ ├── sklearn.png │ │ ├── sublime.png │ │ ├── vs_code.png │ │ ├── weight.png │ │ ├── anaconda.png │ │ ├── bootstrap.png │ │ ├── gunicorn.png │ │ ├── matplotlib.jpg │ │ └── loan-approved.jpg └── css │ ├── gender.min.css │ ├── weight.min.css │ ├── iris.min.css │ ├── weight.css │ ├── gender.sass │ ├── weight.sass │ ├── boston.min.css │ ├── loan.min.css │ ├── gender.css │ ├── iris.sass │ ├── boston.sass │ ├── iris.css │ ├── loan.sass │ ├── boston.css │ ├── loan.css │ ├── navbar_footer │ ├── navbar.min.css │ ├── navbar.css │ ├── navbar.css.map │ └── navbar.sass │ ├── all_projects.min.css │ ├── all_projects.css.map │ ├── all_projects.sass │ ├── all_projects.css │ └── bootstrap-4.5.0 │ ├── bootstrap-reboot.min.css │ └── bootstrap-reboot.css ├── models ├── weight_predictor.pkl ├── gender_classifier.pkl ├── loan_status_predictor.pkl ├── iris_species_classifier.pkl └── boston_house_price_predictor.pkl ├── 04.Weight_Prediction ├── images │ ├── gender_height.png │ ├── gender_weight.png │ ├── feature-relations.png │ ├── modelperformance.png │ ├── feature-correlaion.png │ ├── height_distribution.png │ └── weight_distribution.png ├── Notebooks │ └── .ipynb_checkpoints │ │ └── Weight Prediction-checkpoint.ipynb ├── weight_predictor.py └── README.md ├── 05.Boston_House_Price_Prediction ├── images │ ├── age.png │ ├── b.png │ ├── dis.png │ ├── nox.png │ ├── rad.png │ ├── rm.png │ ├── tax.png │ ├── zn.png │ ├── chas.png │ ├── crim.png │ ├── indus.png │ ├── lstat.png │ ├── medv.png │ ├── pairplot.png │ ├── ptration.png │ ├── feature_importances.png │ ├── features_correlation.png │ ├── correlation_with_price.png │ ├── random_forest_diff_RF_models.png │ ├── random_forest_diff_max_depth.png │ ├── random_decision_tree_regressor.png │ └── random_forest_diff_train_samples.png ├── house_price_predictor.py └── README.md ├── 02.Loan_Status_Prediction ├── images │ ├── term_counts.png │ ├── credit_counts_0.png │ ├── credit_counts_1.png │ ├── credit_counts_2.png │ ├── credit_counts_3.png │ ├── credit_counts_4.png │ ├── credit_counts_5.png │ ├── credit_counts_6.png │ ├── credit_counts_7.png │ ├── credit_counts_8.png │ ├── employed_counts.png │ ├── gender_counts.png │ ├── gender_counts_0.png │ ├── gender_counts_1.png │ ├── gender_counts_2.png │ ├── gender_counts_3.png │ ├── gender_counts_4.png │ ├── gender_counts_5.png │ ├── gender_counts_6.png │ ├── gender_counts_7.png │ ├── gender_counts_8.png │ ├── loan-approved.jpg │ ├── married_counts.png │ ├── employed_counts_0.png │ ├── employed_counts_1.png │ ├── employed_counts_2.png │ ├── employed_counts_3.png │ ├── employed_counts_4.png │ ├── employed_counts_5.png │ ├── employed_counts_6.png │ ├── employed_counts_7.png │ ├── employed_counts_8.png │ ├── loan_amount_term.png │ ├── loan_status_count.png │ ├── married_counts_0.png │ ├── married_counts_1.png │ ├── married_counts_2.png │ ├── married_counts_3.png │ ├── married_counts_4.png │ ├── married_counts_5.png │ ├── married_counts_6.png │ ├── married_counts_7.png │ ├── married_counts_8.png │ ├── property_counts_0.png │ ├── property_counts_1.png │ ├── property_counts_2.png │ ├── property_counts_3.png │ ├── property_counts_4.png │ ├── property_counts_5.png │ ├── property_counts_6.png │ ├── property_counts_7.png │ ├── property_counts_8.png │ ├── dependents_counts_0.png │ ├── dependents_counts_1.png │ ├── dependents_counts_2.png │ ├── dependents_counts_3.png │ ├── dependents_counts_4.png │ ├── dependents_counts_5.png │ ├── dependents_counts_6.png │ ├── dependents_counts_7.png │ ├── dependents_counts_8.png │ ├── education_counts_0.png │ ├── education_counts_1.png │ ├── education_counts_2.png │ ├── education_counts_3.png │ ├── education_counts_4.png │ ├── education_counts_5.png │ ├── education_counts_6.png │ ├── education_counts_7.png │ ├── education_counts_8.png │ ├── feature_importances.png │ ├── gender_description.png │ ├── loan_status_counts.png │ ├── LoanAmountDescription.png │ ├── LoanAmountDistribution.png │ ├── test_data_predictions.png │ ├── correlation_of_features.png │ ├── ApplicantIncomeDistribution.png │ ├── logistic_model_performance.png │ ├── CoapplicantIncomeDistribution.png │ ├── Loan_Amount_Term_value_counts.png │ ├── decision_tree_model_performance.png │ ├── correlation_of_features_with_loan_status.png │ └── decision_tree_classifier_model_performance.png └── loan_status_predictor_model.py ├── 03.Gender_Classifcation ├── images │ ├── gender_height.png │ ├── gender_weight.png │ ├── feature-relations.png │ ├── feature-correlaion.png │ ├── height_distribution.png │ └── weight_distribution.png ├── gender_classifier.py └── README.md ├── 01.Iris_Species_Classification ├── data │ ├── database.sqlite │ └── Iris.csv ├── images │ ├── load_iris.png │ ├── tree_code.png │ ├── iris_species.png │ ├── predictions.png │ ├── decision_tree.png │ ├── iris_species.jpeg │ ├── feature_pairplot.png │ ├── model_performance.png │ ├── feature_correlation.png │ ├── petal_length_width.png │ ├── petal_sepal_length.png │ ├── iris_descriptive_stats.png │ └── petal_width_sepal_length.png ├── iris_species_classifier.py └── README.md ├── py_modules ├── __pycache__ │ └── data_extraction.cpython-37.pyc └── data_extraction.py ├── requirements.txt ├── data ├── social-media-links.csv └── ML_Projects.csv ├── templates ├── weight_predictor.html ├── gender_classifier.html ├── boston_house_price_predictor.html ├── iris_species_classifier.html ├── base.html ├── loan_status_predictor.html └── all_projects.html ├── ml_projects.py ├── LICENSE.txt └── README.md /runtime.txt: -------------------------------------------------------------------------------- 1 | python-3.7.7 -------------------------------------------------------------------------------- /Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn ml_projects:ml_app -------------------------------------------------------------------------------- /Demos/iris.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/Demos/iris.gif -------------------------------------------------------------------------------- /Demos/loan.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/Demos/loan.gif -------------------------------------------------------------------------------- /Demos/main.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/Demos/main.png -------------------------------------------------------------------------------- /Demos/boston.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/Demos/boston.gif -------------------------------------------------------------------------------- /Demos/gender.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/Demos/gender.gif -------------------------------------------------------------------------------- /Demos/weight.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/Demos/weight.gif -------------------------------------------------------------------------------- /static/files/Resume.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/files/Resume.pdf -------------------------------------------------------------------------------- /models/weight_predictor.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/models/weight_predictor.pkl -------------------------------------------------------------------------------- /static/images/icons/a.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/a.ico -------------------------------------------------------------------------------- /static/images/icons/ml.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/ml.png -------------------------------------------------------------------------------- /static/images/icons/www.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/www.png -------------------------------------------------------------------------------- /models/gender_classifier.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/models/gender_classifier.pkl -------------------------------------------------------------------------------- /static/images/icons/blog.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/blog.png -------------------------------------------------------------------------------- /static/images/icons/mail.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/mail.png -------------------------------------------------------------------------------- /static/images/icons/phone.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/phone.png -------------------------------------------------------------------------------- /static/images/photos/ask.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/ask.jpeg -------------------------------------------------------------------------------- /static/images/photos/iris.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/iris.jpg -------------------------------------------------------------------------------- /models/loan_status_predictor.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/models/loan_status_predictor.pkl -------------------------------------------------------------------------------- /static/images/icons/github.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/github.png -------------------------------------------------------------------------------- /static/images/icons/kaggle.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/kaggle.png -------------------------------------------------------------------------------- /static/images/icons/location.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/location.png -------------------------------------------------------------------------------- /static/images/icons/twitter.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/twitter.png -------------------------------------------------------------------------------- /static/images/icons/whatsapp.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/whatsapp.png -------------------------------------------------------------------------------- /static/images/icons/youtube.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/youtube.png -------------------------------------------------------------------------------- /static/images/photos/flask.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/flask.png -------------------------------------------------------------------------------- /static/images/photos/fork_it.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/fork_it.png -------------------------------------------------------------------------------- /static/images/photos/gender.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/gender.png -------------------------------------------------------------------------------- /static/images/photos/house.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/house.jpg -------------------------------------------------------------------------------- /static/images/photos/numpy.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/numpy.png -------------------------------------------------------------------------------- /static/images/photos/pandas.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/pandas.png -------------------------------------------------------------------------------- /static/images/photos/python.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/python.jpeg -------------------------------------------------------------------------------- /static/images/photos/python.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/python.png -------------------------------------------------------------------------------- /static/images/photos/seaborn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/seaborn.png -------------------------------------------------------------------------------- /static/images/photos/sklearn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/sklearn.png -------------------------------------------------------------------------------- /static/images/photos/sublime.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/sublime.png -------------------------------------------------------------------------------- /static/images/photos/vs_code.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/vs_code.png -------------------------------------------------------------------------------- /static/images/photos/weight.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/weight.png -------------------------------------------------------------------------------- /models/iris_species_classifier.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/models/iris_species_classifier.pkl -------------------------------------------------------------------------------- /static/images/icons/instagram.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/instagram.png -------------------------------------------------------------------------------- /static/images/icons/linked-in.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/icons/linked-in.png -------------------------------------------------------------------------------- /static/images/photos/anaconda.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/anaconda.png -------------------------------------------------------------------------------- /static/images/photos/bootstrap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/bootstrap.png -------------------------------------------------------------------------------- /static/images/photos/gunicorn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/gunicorn.png -------------------------------------------------------------------------------- /static/css/gender.min.css: -------------------------------------------------------------------------------- 1 | .space{min-height:1rem}.male{color:#0000ff;font-size:4rem}.female{color:#00ff00;font-size:4rem}.status{font-size:2rem} 2 | -------------------------------------------------------------------------------- /static/images/photos/matplotlib.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/matplotlib.jpg -------------------------------------------------------------------------------- /models/boston_house_price_predictor.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/models/boston_house_price_predictor.pkl -------------------------------------------------------------------------------- /static/css/weight.min.css: -------------------------------------------------------------------------------- 1 | .space{min-height:1rem}@media (min-width: 576px){.modal-dialog{max-width:calc(100% - 15rem) !important;margin:1rem auto}} 2 | -------------------------------------------------------------------------------- /static/images/photos/loan-approved.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/static/images/photos/loan-approved.jpg -------------------------------------------------------------------------------- /04.Weight_Prediction/images/gender_height.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/04.Weight_Prediction/images/gender_height.png -------------------------------------------------------------------------------- /04.Weight_Prediction/images/gender_weight.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/04.Weight_Prediction/images/gender_weight.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/age.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/age.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/b.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/b.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/dis.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/dis.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/nox.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/nox.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/rad.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/rad.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/rm.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/rm.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/tax.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/tax.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/zn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/zn.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/term_counts.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/term_counts.png -------------------------------------------------------------------------------- /03.Gender_Classifcation/images/gender_height.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/03.Gender_Classifcation/images/gender_height.png -------------------------------------------------------------------------------- /03.Gender_Classifcation/images/gender_weight.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/03.Gender_Classifcation/images/gender_weight.png -------------------------------------------------------------------------------- /04.Weight_Prediction/images/feature-relations.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/04.Weight_Prediction/images/feature-relations.png -------------------------------------------------------------------------------- /04.Weight_Prediction/images/modelperformance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/04.Weight_Prediction/images/modelperformance.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/chas.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/chas.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/crim.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/crim.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/indus.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/indus.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/lstat.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/lstat.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/medv.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/medv.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/data/database.sqlite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/data/database.sqlite -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/load_iris.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/load_iris.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/tree_code.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/tree_code.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_0.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_1.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_2.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_3.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_4.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_5.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_6.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_7.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/credit_counts_8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/credit_counts_8.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_0.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_1.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_2.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_3.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_4.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_5.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_6.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_7.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_counts_8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_counts_8.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/loan-approved.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/loan-approved.jpg -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts.png -------------------------------------------------------------------------------- /03.Gender_Classifcation/images/feature-relations.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/03.Gender_Classifcation/images/feature-relations.png -------------------------------------------------------------------------------- /04.Weight_Prediction/images/feature-correlaion.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/04.Weight_Prediction/images/feature-correlaion.png -------------------------------------------------------------------------------- /04.Weight_Prediction/images/height_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/04.Weight_Prediction/images/height_distribution.png -------------------------------------------------------------------------------- /04.Weight_Prediction/images/weight_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/04.Weight_Prediction/images/weight_distribution.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/pairplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/pairplot.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/ptration.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/ptration.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/iris_species.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/iris_species.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/predictions.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/predictions.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_0.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_1.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_2.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_3.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_4.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_5.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_6.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_7.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/employed_counts_8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/employed_counts_8.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/loan_amount_term.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/loan_amount_term.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/loan_status_count.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/loan_status_count.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_0.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_1.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_2.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_3.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_4.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_5.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_6.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_7.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/married_counts_8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/married_counts_8.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_0.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_1.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_2.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_3.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_4.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_5.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_6.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_7.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/property_counts_8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/property_counts_8.png -------------------------------------------------------------------------------- /03.Gender_Classifcation/images/feature-correlaion.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/03.Gender_Classifcation/images/feature-correlaion.png -------------------------------------------------------------------------------- /03.Gender_Classifcation/images/height_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/03.Gender_Classifcation/images/height_distribution.png -------------------------------------------------------------------------------- /03.Gender_Classifcation/images/weight_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/03.Gender_Classifcation/images/weight_distribution.png -------------------------------------------------------------------------------- /04.Weight_Prediction/Notebooks/.ipynb_checkpoints/Weight Prediction-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 4 6 | } 7 | -------------------------------------------------------------------------------- /py_modules/__pycache__/data_extraction.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/py_modules/__pycache__/data_extraction.cpython-37.pyc -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/decision_tree.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/decision_tree.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/iris_species.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/iris_species.jpeg -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_0.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_1.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_2.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_3.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_4.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_5.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_6.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_7.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/dependents_counts_8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/dependents_counts_8.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_0.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_1.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_2.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_3.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_4.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_5.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_6.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_7.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/education_counts_8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/education_counts_8.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/feature_importances.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/feature_importances.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/gender_description.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/gender_description.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/loan_status_counts.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/loan_status_counts.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/feature_pairplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/feature_pairplot.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/model_performance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/model_performance.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/LoanAmountDescription.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/LoanAmountDescription.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/LoanAmountDistribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/LoanAmountDistribution.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/test_data_predictions.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/test_data_predictions.png -------------------------------------------------------------------------------- /static/css/iris.min.css: -------------------------------------------------------------------------------- 1 | .space{min-height:1rem}.setosa{color:#4c72b0;font-size:5rem}.versicolor{color:#dd8452;font-size:4rem}.virginica{color:#55a867;font-size:4rem}.status{font-size:2rem} 2 | -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/feature_correlation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/feature_correlation.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/petal_length_width.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/petal_length_width.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/petal_sepal_length.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/petal_sepal_length.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/correlation_of_features.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/correlation_of_features.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/iris_descriptive_stats.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/iris_descriptive_stats.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/ApplicantIncomeDistribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/ApplicantIncomeDistribution.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/logistic_model_performance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/logistic_model_performance.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/feature_importances.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/feature_importances.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/features_correlation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/features_correlation.png -------------------------------------------------------------------------------- /01.Iris_Species_Classification/images/petal_width_sepal_length.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/01.Iris_Species_Classification/images/petal_width_sepal_length.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/CoapplicantIncomeDistribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/CoapplicantIncomeDistribution.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/Loan_Amount_Term_value_counts.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/Loan_Amount_Term_value_counts.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/correlation_with_price.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/correlation_with_price.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/decision_tree_model_performance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/decision_tree_model_performance.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/random_forest_diff_RF_models.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/random_forest_diff_RF_models.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/random_forest_diff_max_depth.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/random_forest_diff_max_depth.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/random_decision_tree_regressor.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/random_decision_tree_regressor.png -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/correlation_of_features_with_loan_status.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/correlation_of_features_with_loan_status.png -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/images/random_forest_diff_train_samples.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/05.Boston_House_Price_Prediction/images/random_forest_diff_train_samples.png -------------------------------------------------------------------------------- /static/css/weight.css: -------------------------------------------------------------------------------- 1 | .space { 2 | min-height: 1rem; 3 | } 4 | 5 | @media (min-width: 576px) { 6 | .modal-dialog { 7 | max-width: calc(100% - 15rem) !important; 8 | margin: 1rem auto; 9 | } 10 | } 11 | -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/images/decision_tree_classifier_model_performance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ASHOKKUMAR-K/Machine-Learning-Projects/HEAD/02.Loan_Status_Prediction/images/decision_tree_classifier_model_performance.png -------------------------------------------------------------------------------- /static/css/gender.sass: -------------------------------------------------------------------------------- 1 | .space 2 | min-height: 1rem 3 | 4 | .male 5 | color: #0000ff 6 | font-size: 4rem 7 | 8 | .female 9 | color: #00ff00 10 | font-size: 4rem 11 | 12 | .status 13 | font-size: 2rem -------------------------------------------------------------------------------- /static/css/weight.sass: -------------------------------------------------------------------------------- 1 | .space 2 | min-height: 1rem 3 | 4 | 5 | 6 | @media (min-width: 576px) 7 | .modal-dialog 8 | max-width: calc(100% - 15rem) !important 9 | margin: 1rem auto 10 | 11 | -------------------------------------------------------------------------------- /static/css/boston.min.css: -------------------------------------------------------------------------------- 1 | .space{min-height:1rem}.boston{font-size:2rem !important}.boston-price{font-size:4rem !important;color:#0000ff !important}@media (min-width: 576px){.modal-dialog{max-width:calc(100% - 30rem) !important;margin:1rem auto}} 2 | -------------------------------------------------------------------------------- /static/css/loan.min.css: -------------------------------------------------------------------------------- 1 | .space{min-height:1rem}.congrats{color:#00ff00;font-size:5rem}.rejects{color:#ff0000;font-size:4rem}.status{font-size:2rem}@media (min-width: 576px){.modal-dialog{max-width:calc(100% - 15rem) !important;margin:1rem auto}} 2 | -------------------------------------------------------------------------------- /static/css/gender.css: -------------------------------------------------------------------------------- 1 | .space { 2 | min-height: 1rem; 3 | } 4 | 5 | .male { 6 | color: #0000ff; 7 | font-size: 4rem; 8 | } 9 | 10 | .female { 11 | color: #00ff00; 12 | font-size: 4rem; 13 | } 14 | 15 | .status { 16 | font-size: 2rem; 17 | } 18 | -------------------------------------------------------------------------------- /static/css/iris.sass: -------------------------------------------------------------------------------- 1 | .space 2 | min-height: 1rem 3 | 4 | .setosa 5 | color: #4c72b0 6 | font-size: 5rem 7 | 8 | .versicolor 9 | color: #dd8452 10 | font-size: 4rem 11 | 12 | .virginica 13 | color: #55a867 14 | font-size: 4rem 15 | 16 | .status 17 | font-size: 2rem -------------------------------------------------------------------------------- /static/css/boston.sass: -------------------------------------------------------------------------------- 1 | .space 2 | min-height: 1rem 3 | 4 | .boston 5 | font-size: 2rem !important 6 | 7 | .boston-price 8 | font-size: 4rem !important 9 | color: #0000ff !important 10 | 11 | @media (min-width: 576px) 12 | .modal-dialog 13 | max-width: calc(100% - 30rem) !important 14 | margin: 1rem auto -------------------------------------------------------------------------------- /static/css/iris.css: -------------------------------------------------------------------------------- 1 | .space { 2 | min-height: 1rem; 3 | } 4 | 5 | .setosa { 6 | color: #4c72b0; 7 | font-size: 5rem; 8 | } 9 | 10 | .versicolor { 11 | color: #dd8452; 12 | font-size: 4rem; 13 | } 14 | 15 | .virginica { 16 | color: #55a867; 17 | font-size: 4rem; 18 | } 19 | 20 | .status { 21 | font-size: 2rem; 22 | } 23 | -------------------------------------------------------------------------------- /static/css/loan.sass: -------------------------------------------------------------------------------- 1 | .space 2 | min-height: 1rem 3 | 4 | .congrats 5 | color: #00ff00 6 | font-size: 5rem 7 | 8 | .rejects 9 | color: #ff0000 10 | font-size: 4rem 11 | 12 | .status 13 | font-size: 2rem 14 | 15 | @media (min-width: 576px) 16 | .modal-dialog 17 | max-width: calc(100% - 15rem) !important 18 | margin: 1rem auto -------------------------------------------------------------------------------- /static/css/boston.css: -------------------------------------------------------------------------------- 1 | .space { 2 | min-height: 1rem; 3 | } 4 | 5 | .boston { 6 | font-size: 2rem !important; 7 | } 8 | 9 | .boston-price { 10 | font-size: 4rem !important; 11 | color: #0000ff !important; 12 | } 13 | 14 | @media (min-width: 576px) { 15 | .modal-dialog { 16 | max-width: calc(100% - 30rem) !important; 17 | margin: 1rem auto; 18 | } 19 | } 20 | -------------------------------------------------------------------------------- /static/css/loan.css: -------------------------------------------------------------------------------- 1 | .space { 2 | min-height: 1rem; 3 | } 4 | 5 | .congrats { 6 | color: #00ff00; 7 | font-size: 5rem; 8 | } 9 | 10 | .rejects { 11 | color: #ff0000; 12 | font-size: 4rem; 13 | } 14 | 15 | .status { 16 | font-size: 2rem; 17 | } 18 | 19 | @media (min-width: 576px) { 20 | .modal-dialog { 21 | max-width: calc(100% - 15rem) !important; 22 | margin: 1rem auto; 23 | } 24 | } 25 | -------------------------------------------------------------------------------- /static/css/navbar_footer/navbar.min.css: -------------------------------------------------------------------------------- 1 | body{user-select:none}.navbar-color{background-color:#6f4a9a}.navbar-expand-lg .navbar-nav .nav-link{padding-left:1rem;padding-right:1rem;font-size:1rem}.navbar{padding:.5rem 1rem}.my_name{padding-left:0rem;margin-top:0.3rem;font-size:1.7rem}ul{list-style:none;padding:0px;text-align:left}.footer,a,a:hover{color:#fff;text-decoration:none}.social-links li{padding:0.2rem}.text-white{color:#fff}.navbar-toggler{padding:0.3rem}.content{padding:2rem;min-height:70vh} 2 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | beautifulsoup4==4.9.0 2 | click==7.1.2 3 | cycler==0.10.0 4 | distributed==2.16.0 5 | Flask==1.1.2 6 | gunicorn==20.0.4 7 | itsdangerous==1.1.0 8 | Jinja2==2.11.2 9 | joblib==0.14.1 10 | json5==0.9.4 11 | jsonschema==3.2.0 12 | kiwisolver==1.2.0 13 | Markdown==3.2.1 14 | MarkupSafe==1.1.1 15 | matplotlib==3.1.3 16 | numpy==1.18.1 17 | pandas==1.0.3 18 | pickleshare==0.7.5 19 | python-dateutil==2.8.1 20 | python-jsonrpc-server==0.3.4 21 | python-language-server==0.31.10 22 | pytz==2020.1 23 | PyYAML==5.3.1 24 | requests==2.23.0 25 | requests-oauthlib==1.3.0 26 | scikit-learn==0.22.1 27 | scipy==1.4.1 28 | seaborn==0.10.1 29 | six==1.14.0 30 | statsmodels==0.11.0 31 | urllib3==1.25.8 32 | Werkzeug==1.0.1 33 | -------------------------------------------------------------------------------- /py_modules/data_extraction.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | 4 | def get_social_link_data(social): 5 | ''' 6 | Returns a NumPy array of social data 7 | ''' 8 | 9 | # Extracting Social Link Name 10 | social_name = social.iloc[:, 0].values 11 | 12 | # Extracting Social Link 13 | social_link = social.iloc[:, 1].values 14 | 15 | # Extracting Social Links Text 16 | social_text = social.iloc[:, 2].values 17 | 18 | # Extracting Social Logo path 19 | social_logo_path = social.iloc[:, 3].values 20 | 21 | return np.asarray([social_name, social_link, social_text, social_logo_path]) 22 | 23 | def get_ml_projects_data(ml_projects): 24 | # Sorting to bring recent projects at the beginning 25 | ml_projects.sort_values(by = 'Project_No', ascending = False, inplace = True) 26 | 27 | return ml_projects.T.values -------------------------------------------------------------------------------- /data/social-media-links.csv: -------------------------------------------------------------------------------- 1 | Media,Links,Links_Text,Icons 2 | Location,#,Chennai,images/icons/location.png 3 | Contact,#,+91 6382040562,images/icons/phone.png 4 | WhatsApp,#,+91 8056224273,images/icons/whatsapp.png 5 | Email,mailto:ashokkumark.citeee2017@gmail.com,Gmail,images/icons/mail.png 6 | GitHub,https://github.com/ASHOKKUMAR-K,GitHub,images/icons/github.png 7 | YouTube,https://www.youtube.com/,YouTube,images/icons/youtube.png 8 | Medium,https://medium.com/@Ashok_kumar_K,Medium,images/icons/blog.png 9 | Linkedin,https://www.linkedin.com/in/ashok-kumar-k-6641bb16a,Linkedin,images/icons/linked-in.png 10 | Kaggle,https://www.kaggle.com/ashokkumar1085,Kaggle,images/icons/kaggle.png 11 | Instagram,https://www.instagram.com/ashok0487,Instagram,images/icons/instagram.png 12 | Twitter,https://twitter.com/ASHOKKU90871834,Twitter,images/icons/twitter.png -------------------------------------------------------------------------------- /static/css/navbar_footer/navbar.css: -------------------------------------------------------------------------------- 1 | body { 2 | user-select: none; 3 | } 4 | 5 | .navbar-color { 6 | background-color: #6f4a9a; 7 | } 8 | 9 | .navbar-expand-lg .navbar-nav .nav-link { 10 | padding-left: 1rem; 11 | padding-right: 1rem; 12 | font-size: 1rem; 13 | } 14 | 15 | .navbar { 16 | padding: 0.5rem 1rem; 17 | } 18 | 19 | .my_name { 20 | padding-left: 0rem; 21 | margin-top: 0.3rem; 22 | font-size: 1.7rem; 23 | } 24 | 25 | ul { 26 | list-style: none; 27 | padding: 0px; 28 | text-align: left; 29 | } 30 | 31 | .footer, a, a:hover { 32 | color: #ffffff; 33 | text-decoration: none; 34 | } 35 | 36 | .social-links li { 37 | padding: 0.2rem; 38 | } 39 | 40 | .text-white { 41 | color: #ffffff; 42 | } 43 | 44 | .navbar-toggler { 45 | padding: 0.3rem; 46 | } 47 | 48 | .content { 49 | padding: 2rem; 50 | min-height: 70vh; 51 | } 52 | -------------------------------------------------------------------------------- /static/css/navbar_footer/navbar.css.map: -------------------------------------------------------------------------------- 1 | { 2 | "version": 3, 3 | "mappings": "AAOA,AAAA,IAAI,CAAC;EACD,WAAW,EAAE,IAAI;CAAG;;AAIxB,AAAA,aAAa,CAAC;EACV,gBAAgB,EAbH,OAAO;CAae;;AAEvC,AAAA,iBAAiB,CAAC,WAAW,CAAC,SAAS,CAAC;EACpC,YAAY,EAfK,IAAI;EAgBrB,aAAa,EAhBI,IAAI;EAiBrB,SAAS,EAAE,IAAI;CAAG;;AAEtB,AAAA,OAAO,CAAC;EACJ,OAAO,EAnBU,MAAM,CADN,IAAI;CAoB8B;;AAEvD,AAAA,QAAQ,CAAC;EACL,YAAY,EAAE,IAAI;EAClB,UAAU,EAAE,MAAM;EAClB,SAAS,EAAE,MAAM;CAAG;;AAExB,AAAA,EAAE,CAAC;EACC,UAAU,EAAE,IAAI;EAChB,OAAO,EAAE,GAAG;EACZ,UAAU,EAAE,IAAI;CAAG;;AAEvB,AAAA,OAAO,EAAE,CAAC,EAAE,CAAC,AAAA,MAAM,CAAC;EAChB,KAAK,EA9BI,OAAO;EA+BhB,eAAe,EAAE,IAAI;CAAG;;AAE5B,AAAA,aAAa,CAAC,EAAE,CAAC;EACb,OAAO,EAAE,MAAM;CAAG;;AAEtB,AAAA,WAAW,CAAC;EACR,KAAK,EArCI,OAAO;CAqCK;;AAEzB,AAAA,eAAe,CAAC;EACZ,OAAO,EAAE,MAAM;CAAG", 4 | "sources": [ 5 | "navbar.sass" 6 | ], 7 | "names": [], 8 | "file": "navbar.css" 9 | } -------------------------------------------------------------------------------- /static/css/all_projects.min.css: -------------------------------------------------------------------------------- 1 | .img-top-content{position:relative}.img-front{max-height:16rem;opacity:1;display:block;transition:.5s ease;backface-visibility:hidden}.centered-content{transition:.5s ease;opacity:0;position:absolute;top:50%;left:50%;transform:translate(-50%, -50%);text-align:center}.img-top-content:hover .img-front{opacity:0.6}.img-top-content:hover .centered-content{opacity:1}.card-title{margin:0px;padding-top:1rem}.modal-body{padding:2rem}.date{justify-content:center;padding:1.5rem 0rem}.date-content{font-weight:bolder;text-align:center}@media (min-width: 576px){.modal-dialog{max-width:calc(100% - 15rem) !important;margin:1rem auto}}.space{min-height:2rem}.algo{color:#00f !important}.pad{max-height:8rem;max-width:7rem;margin:1rem 1.5rem}.side{padding:0.5rem;border-radius:0.3rem;width:100%}.launch{padding:1rem !important;font-weight:bold;margin-bottom:1rem;width:100%}.project{margin:1rem auto}.shadow-effects{box-shadow:0rem 0rem 1.5rem rgba(0,0,0,0.4) !important}.para-content{text-align:justify} 2 | -------------------------------------------------------------------------------- /static/css/all_projects.css.map: -------------------------------------------------------------------------------- 1 | { 2 | "version": 3, 3 | "mappings": "AAAA,AAAA,QAAQ,CAAC;EACL,OAAO,EAAE,IAAI;EACb,UAAU,EAAE,IAAI;CAAG;;AAEvB,AAAA,gBAAgB,CAAC;EACb,QAAQ,EAAE,QAAQ;CACpB;;AAEF,AAAA,UAAU,CAAC;EACP,UAAU,EAAE,KAAK;EACjB,OAAO,EAAE,CAAC;EACV,OAAO,EAAE,KAAK;EACd,UAAU,EAAE,QAAQ;EACpB,mBAAmB,EAAE,MAAM;CAAG;;AAElC,AAAA,iBAAiB,CAAC;EACd,UAAU,EAAE,QAAQ;EACpB,OAAO,EAAE,CAAC;EACV,QAAQ,EAAE,QAAQ;EAClB,GAAG,EAAE,GAAG;EACR,IAAI,EAAE,GAAG;EACT,SAAS,EAAE,qBAAqB;EAChC,UAAU,EAAE,MAAM;CAAG;;AAEzB,AAAA,gBAAgB,AAAA,MAAM,CAAC,UAAU,CAAC;EAC9B,OAAO,EAAE,GAAG;CAAG;;AAEnB,AAAA,gBAAgB,AAAA,MAAM,CAAC,iBAAiB,CAAC;EACrC,OAAO,EAAE,CAAC;CAAG;;AAEjB,AAAA,WAAW,CAAC;EACR,MAAM,EAAE,GAAG;EACX,WAAW,EAAE,IAAI;CAAG;;AAExB,AAAA,WAAW,CAAC;EACR,OAAO,EAAE,IAAI;CAAG;;AAEpB,AAAA,KAAK,CAAC;EACF,eAAe,EAAE,MAAM;CAAG;;AAE9B,MAAM,EAAE,SAAS,EAAE,KAAK;EACpB,AAAA,aAAa,CAAC;IACV,SAAS,EAAE,kBAAkB,CAAC,UAAU;IACxC,MAAM,EAAE,SAAS;GAAG", 4 | "sources": [ 5 | "all_projects.sass" 6 | ], 7 | "names": [], 8 | "file": "all_projects.css" 9 | } -------------------------------------------------------------------------------- /static/css/navbar_footer/navbar.sass: -------------------------------------------------------------------------------- 1 | $primary-color : #6f4a9a 2 | $padding-left-right: 1rem 3 | $padding-top-bottom: 0.5rem 4 | $light-white: #cccccc 5 | $full-white: #ffffff 6 | 7 | 8 | body 9 | user-select: none 10 | 11 | 12 | 13 | .navbar-color 14 | background-color: $primary-color 15 | 16 | .navbar-expand-lg .navbar-nav .nav-link 17 | padding-left: $padding-left-right 18 | padding-right: $padding-left-right 19 | font-size: 1rem 20 | 21 | .navbar 22 | padding: $padding-top-bottom $padding-left-right 23 | 24 | .my_name 25 | padding-left: 0rem 26 | margin-top: 0.3rem 27 | font-size: 1.7rem 28 | 29 | ul 30 | list-style: none 31 | padding: 0px 32 | text-align: left 33 | 34 | .footer, a, a:hover 35 | color: $full-white 36 | text-decoration: none 37 | 38 | .social-links li 39 | padding: 0.2rem 40 | 41 | .text-white 42 | color: $full-white 43 | 44 | .navbar-toggler 45 | padding: 0.3rem 46 | 47 | .content 48 | padding: 2rem 49 | min-height: 70vh 50 | -------------------------------------------------------------------------------- /static/images/icons/a.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | -------------------------------------------------------------------------------- /03.Gender_Classifcation/gender_classifier.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Jul 9 22:23:54 2020 5 | 6 | @author: ashokubuntu 7 | """ 8 | # Importing Packages 9 | import pandas as pd 10 | import numpy as np 11 | 12 | from sklearn.pipeline import make_pipeline 13 | from sklearn.preprocessing import StandardScaler 14 | from sklearn.svm import SVC 15 | from sklearn.model_selection import train_test_split 16 | 17 | import pickle 18 | # Loading Weight Height Data 19 | wh = pd.read_csv('data/weight-height.csv') 20 | 21 | # Extracting Prediction and Features values 22 | X = wh.iloc[:, [1, 2]].values 23 | y = wh.iloc[:, 0].values 24 | 25 | # Splitting data for training and testing 26 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, 27 | random_state = 42) 28 | 29 | # Creating Support Vector Machine classifier Model 30 | gender_classifier = make_pipeline(StandardScaler(), SVC(gamma='auto', probability = True)) 31 | gender_classifier.fit(X_train, y_train) 32 | 33 | # Dumping Model to a pickle file 34 | pickle.dump(gender_classifier, open("../models/gender_classifier.pkl", "wb")) -------------------------------------------------------------------------------- /04.Weight_Prediction/weight_predictor.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Jul 9 22:57:58 2020 5 | 6 | @author: ashokubuntu 7 | """ 8 | 9 | # Importing Packages 10 | import pandas as pd 11 | import numpy as np 12 | 13 | from sklearn.pipeline import make_pipeline 14 | from sklearn.preprocessing import StandardScaler 15 | from sklearn.linear_model import LinearRegression 16 | from sklearn.model_selection import train_test_split 17 | 18 | import pickle 19 | # Loading Weight Height Data 20 | wh = pd.read_csv('data/weight-height.csv') 21 | 22 | # Converting Categorical values to Numerical values 23 | wh['Gender'] = wh['Gender'].apply(lambda x : {'Male' : 1, 'Female' : 0}[x]) 24 | 25 | # Extracting Prediction and Features values 26 | X = wh.iloc[:, [1, 0]].values 27 | y = wh.iloc[:, 2].values 28 | 29 | # Splitting data for training and testing 30 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, 31 | random_state = 42) 32 | 33 | # Creating Support Vector Machine classifier Model 34 | weight_predictor = make_pipeline(StandardScaler(), LinearRegression()) 35 | weight_predictor.fit(X_train, y_train) 36 | 37 | # Dumping Model to a pickle file 38 | pickle.dump(weight_predictor, open("../models/weight_predictor.pkl", "wb")) -------------------------------------------------------------------------------- /01.Iris_Species_Classification/iris_species_classifier.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Jul 9 18:24:11 2020 5 | 6 | @author: ashokubuntu 7 | """ 8 | 9 | 10 | # Importing Packages 11 | import numpy as np 12 | import pandas as pd 13 | from sklearn.model_selection import train_test_split 14 | from sklearn.tree import DecisionTreeClassifier 15 | 16 | import pickle 17 | 18 | # Loading Data 19 | iris = pd.read_csv('data/Iris.csv') 20 | 21 | # Extracting Features and prediction column 22 | predictions = ['PetalLengthCm', 'PetalWidthCm', 'Species'] 23 | 24 | # Selecting Iris Features 25 | iris_features = iris.loc[:, ['PetalLengthCm', 'PetalWidthCm']] 26 | # Selecting Iris Species 27 | iris_species = iris.loc[:, 'Species'] 28 | 29 | # Converting Pandas DataFrames and Series into Numpy arrays 30 | features_values = iris_features.values 31 | species_values = iris_species.values 32 | 33 | # TODO : Split the dataset as Train set and Test set. 34 | X_train, X_test, y_train, y_test = train_test_split(features_values, species_values, 35 | test_size = 0.25, random_state = 42) 36 | 37 | # TODO : Build Decision Tree Classifier Model 38 | iris_classifier = DecisionTreeClassifier(criterion = 'gini') 39 | iris_classifier.fit(X_train, y_train) 40 | 41 | # Dumping Model to a pickle file 42 | pickle.dump(iris_classifier, open("../models/iris_species_classifier.pkl", "wb")) -------------------------------------------------------------------------------- /static/css/all_projects.sass: -------------------------------------------------------------------------------- 1 | 2 | .img-top-content 3 | position: relative 4 | // width: 50% 5 | 6 | .img-front 7 | max-height: 16rem 8 | opacity: 1 9 | display: block 10 | transition: .5s ease 11 | backface-visibility: hidden 12 | 13 | .centered-content 14 | transition: .5s ease 15 | opacity: 0 16 | position: absolute 17 | top: 50% 18 | left: 50% 19 | transform: translate(-50%, -50%) 20 | text-align: center 21 | 22 | .img-top-content:hover .img-front 23 | opacity: 0.6 24 | 25 | .img-top-content:hover .centered-content 26 | opacity: 1 27 | 28 | .card-title 29 | margin: 0px 30 | padding-top: 1rem 31 | 32 | .modal-body 33 | padding: 2rem 34 | 35 | .date 36 | justify-content: center 37 | padding: 1.5rem 0rem 38 | 39 | .date-content 40 | font-weight: bolder 41 | text-align: center 42 | 43 | @media (min-width: 576px) 44 | .modal-dialog 45 | max-width: calc(100% - 15rem) !important 46 | margin: 1rem auto 47 | 48 | .space 49 | min-height: 2rem 50 | 51 | .algo 52 | color: #00f !important 53 | 54 | .pad 55 | max-height: 8rem 56 | max-width: 7rem 57 | margin: 1rem 1.5rem 58 | 59 | .side 60 | padding: 0.5rem 61 | border-radius: 0.3rem 62 | width: 100% 63 | 64 | .launch 65 | padding: 1rem !important 66 | font-weight: bold 67 | margin-bottom: 1rem 68 | width: 100% 69 | 70 | .project 71 | margin: 1rem auto 72 | 73 | .shadow-effects 74 | box-shadow: 0rem 0rem 1.5rem rgba(0,0,0,0.4) !important 75 | 76 | .para-content 77 | text-align: justify -------------------------------------------------------------------------------- /static/css/all_projects.css: -------------------------------------------------------------------------------- 1 | .img-top-content { 2 | position: relative; 3 | } 4 | 5 | .img-front { 6 | max-height: 16rem; 7 | opacity: 1; 8 | display: block; 9 | transition: .5s ease; 10 | backface-visibility: hidden; 11 | } 12 | 13 | .centered-content { 14 | transition: .5s ease; 15 | opacity: 0; 16 | position: absolute; 17 | top: 50%; 18 | left: 50%; 19 | transform: translate(-50%, -50%); 20 | text-align: center; 21 | } 22 | 23 | .img-top-content:hover .img-front { 24 | opacity: 0.6; 25 | } 26 | 27 | .img-top-content:hover .centered-content { 28 | opacity: 1; 29 | } 30 | 31 | .card-title { 32 | margin: 0px; 33 | padding-top: 1rem; 34 | } 35 | 36 | .modal-body { 37 | padding: 2rem; 38 | } 39 | 40 | .date { 41 | justify-content: center; 42 | padding: 1.5rem 0rem; 43 | } 44 | 45 | .date-content { 46 | font-weight: bolder; 47 | text-align: center; 48 | } 49 | 50 | @media (min-width: 576px) { 51 | .modal-dialog { 52 | max-width: calc(100% - 15rem) !important; 53 | margin: 1rem auto; 54 | } 55 | } 56 | 57 | .space { 58 | min-height: 2rem; 59 | } 60 | 61 | .algo { 62 | color: #00f !important; 63 | } 64 | 65 | .pad { 66 | max-height: 8rem; 67 | max-width: 7rem; 68 | margin: 1rem 1.5rem; 69 | } 70 | 71 | .side { 72 | padding: 0.5rem; 73 | border-radius: 0.3rem; 74 | width: 100%; 75 | } 76 | 77 | .launch { 78 | padding: 1rem !important; 79 | font-weight: bold; 80 | margin-bottom: 1rem; 81 | width: 100%; 82 | } 83 | 84 | .project { 85 | margin: 1rem auto; 86 | } 87 | 88 | .shadow-effects { 89 | box-shadow: 0rem 0rem 1.5rem rgba(0, 0, 0, 0.4) !important; 90 | } 91 | 92 | .para-content { 93 | text-align: justify; 94 | } 95 | -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/house_price_predictor.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Sat Jul 11 14:49:51 2020 5 | 6 | @author: ashokubuntu 7 | """ 8 | 9 | # Importing Packages 10 | import pandas as pd 11 | import numpy as np 12 | 13 | from sklearn.model_selection import train_test_split 14 | from sklearn.ensemble import RandomForestRegressor 15 | 16 | import pickle 17 | 18 | # Loading Data 19 | columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 20 | 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] 21 | boston = pd.read_csv('data/housing.csv', delimiter=r"\s+", 22 | names = columns) 23 | 24 | # Selecting Top 6 Features 25 | top_6_features = ['LSTAT', 'RM', 'PTRATIO', 'INDUS', 'TAX', 'NOX'] 26 | 27 | # Extracting top features values 28 | features = boston.loc[:, top_6_features].values 29 | 30 | # Extracting Prediction values 31 | predictions = boston.loc[:, 'MEDV'].values 32 | 33 | # Splitting Data into train and test 34 | # Choosing Optimal Training Samples 35 | X_train, X_test, y_train, y_test = train_test_split(features, 36 | predictions, 37 | test_size = 0.20, 38 | random_state = 3) 39 | 40 | # Building Optimal Random Forest regressor Model 41 | random_forest_regressor = RandomForestRegressor(max_depth = 13, 42 | random_state = 68) 43 | random_forest_regressor.fit(X_train, y_train) 44 | 45 | # Dumping Model to a pickle file 46 | pickle.dump(random_forest_regressor, open("../models/boston_house_price_predictor.pkl", "wb")) -------------------------------------------------------------------------------- /02.Loan_Status_Prediction/loan_status_predictor_model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Jul 9 07:36:14 2020 5 | 6 | @author: ashokubuntu 7 | """ 8 | 9 | # Importing Packages 10 | import numpy as np 11 | import pandas as pd 12 | from sklearn.tree import DecisionTreeClassifier 13 | 14 | import pickle 15 | 16 | 17 | # TODO : Filling Above values on LoanAmount column based on LoanStatus. 18 | def LoanAmount_null_values_filler(df): 19 | ''' Fills null value on LoanAmount based on Loan_Status ''' 20 | for row in range(df.shape[0]): 21 | if pd.isnull(df.loc[row, 'LoanAmount']): 22 | if df.loc[row, 'Loan_Status'] == 'Y': 23 | df.loc[row, 'LoanAmount'] = 151.22 24 | elif df.loc[row, 'Loan_Status'] == 'N': 25 | df.loc[row, 'LoanAmount'] = 144.29 26 | else: 27 | pass 28 | return df 29 | 30 | # TODO : Function for filling null values on dependents columns 31 | def dependents_null_value_filler(df): 32 | for row in range(df.shape[0]): 33 | if df.loc[row, 'Dependents'] is np.nan: 34 | df.loc[row, 'Dependents'] = str(df.loc[row, 'Married']) 35 | return df 36 | 37 | # Loading Train data 38 | train_data = pd.read_csv('data/train_u6lujuX_CVtuZ9i.csv') 39 | 40 | # Initialzing Prediction features 41 | prediction_features = ['Credit_History', 'LoanAmount', 'ApplicantIncome', 'CoapplicantIncome', 'Dependents', 'Loan_Status', 'Married'] 42 | 43 | # Extracting prediction data from the whole data 44 | train_data = train_data.loc[:, prediction_features] 45 | 46 | # TODO : Filling null values on Credit_History 47 | train_data['Credit_History'] = train_data['Credit_History'].fillna(value = 1.0) 48 | 49 | # TODO : Filling null values on LoanAmount 50 | train_data = LoanAmount_null_values_filler(train_data) 51 | 52 | # Let's fill null values in Married columns with 'Yes' 53 | train_data['Married'] = train_data['Married'].fillna('Yes') 54 | 55 | # TODO : encoding categorical values into numerical values 56 | train_data['Married'] = train_data['Married'].apply(lambda x : {'Yes' : 1, 'No' : 0}[x]) 57 | 58 | # TODO : Fill null values on Dependents column 59 | train_data = dependents_null_value_filler(train_data) 60 | 61 | # TODO : Encoding Categorical data into Numerical Data 62 | train_data['Dependents'] = train_data['Dependents'].apply(lambda x : {'0' : 0, '1' : 1, '2' : 2, '3+' : 3}[x]) 63 | 64 | # Drop Married Column 65 | train_data.drop('Married', inplace = True, axis = 1) 66 | 67 | # Extracting Feature Values and Prediction Values 68 | feature_values = train_data.iloc[:, :-1].values 69 | prediction_values = train_data['Loan_Status'].values 70 | 71 | # Creating Decision Tree Classifier Model 72 | decision_tree_model = DecisionTreeClassifier(max_depth = 8) 73 | decision_tree_model.fit(feature_values, prediction_values) 74 | 75 | # Dumping Model to a pickle file 76 | pickle.dump(decision_tree_model, open("../models/loan_status_predictor.pkl", "wb")) -------------------------------------------------------------------------------- /templates/weight_predictor.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | 3 | {% block title %}Weight Predictor{% endblock %} 4 | 5 | {% block css_links %} 6 | 7 | {% endblock %} 8 | 9 | {% block content %} 10 | 11 |
12 |
13 |

WEIGHT PREDICTOR

14 |
15 |
16 |
17 |
18 |
19 |
20 |
21 | 22 | 26 | Person's Gender 27 |
28 |
29 | 30 | 31 | Person's Height in inches 32 |
33 | 34 |
35 |
36 |
37 |
38 |
39 | 40 |
41 |
42 |
43 |
44 | 45 | 46 | 47 | {% if weight_predicted %} 48 | 85 | {% endif %} 86 | 87 | 88 | 89 | 90 |
91 | 92 | 93 | {% endblock %} 94 | 95 | 96 | 97 | {% block js_scripts %} 98 | 103 | {% endblock %} -------------------------------------------------------------------------------- /static/css/bootstrap-4.5.0/bootstrap-reboot.min.css: -------------------------------------------------------------------------------- 1 | /*! 2 | * Bootstrap Reboot v4.5.0 (https://getbootstrap.com/) 3 | * Copyright 2011-2020 The Bootstrap Authors 4 | * Copyright 2011-2020 Twitter, Inc. 5 | * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) 6 | * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md) 7 | */*,::after,::before{box-sizing:border-box}html{font-family:sans-serif;line-height:1.15;-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:transparent}article,aside,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}body{margin:0;font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;text-align:left;background-color:#fff}[tabindex="-1"]:focus:not(:focus-visible){outline:0!important}hr{box-sizing:content-box;height:0;overflow:visible}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:.5rem}p{margin-top:0;margin-bottom:1rem}abbr[data-original-title],abbr[title]{text-decoration:underline;-webkit-text-decoration:underline dotted;text-decoration:underline dotted;cursor:help;border-bottom:0;-webkit-text-decoration-skip-ink:none;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}dl,ol,ul{margin-top:0;margin-bottom:1rem}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem}b,strong{font-weight:bolder}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:#007bff;text-decoration:none;background-color:transparent}a:hover{color:#0056b3;text-decoration:underline}a:not([href]){color:inherit;text-decoration:none}a:not([href]):hover{color:inherit;text-decoration:none}code,kbd,pre,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em}pre{margin-top:0;margin-bottom:1rem;overflow:auto;-ms-overflow-style:scrollbar}figure{margin:0 0 1rem}img{vertical-align:middle;border-style:none}svg{overflow:hidden;vertical-align:middle}table{border-collapse:collapse}caption{padding-top:.75rem;padding-bottom:.75rem;color:#6c757d;text-align:left;caption-side:bottom}th{text-align:inherit}label{display:inline-block;margin-bottom:.5rem}button{border-radius:0}button:focus{outline:1px dotted;outline:5px auto -webkit-focus-ring-color}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,input{overflow:visible}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled),button:not(:disabled){cursor:pointer}[type=button]::-moz-focus-inner,[type=reset]::-moz-focus-inner,[type=submit]::-moz-focus-inner,button::-moz-focus-inner{padding:0;border-style:none}input[type=checkbox],input[type=radio]{box-sizing:border-box;padding:0}textarea{overflow:auto;resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;max-width:100%;padding:0;margin-bottom:.5rem;font-size:1.5rem;line-height:inherit;color:inherit;white-space:normal}progress{vertical-align:baseline}[type=number]::-webkit-inner-spin-button,[type=number]::-webkit-outer-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:none}[type=search]::-webkit-search-decoration{-webkit-appearance:none}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}summary{display:list-item;cursor:pointer}template{display:none}[hidden]{display:none!important} 8 | /*# sourceMappingURL=bootstrap-reboot.min.css.map */ -------------------------------------------------------------------------------- /templates/gender_classifier.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | 3 | {% block title %}Gender Classifier{% endblock %} 4 | 5 | {% block css_links %} 6 | 7 | 8 | {% if gender_predicted %} 9 | 10 | 39 | 40 | {% endif %} 41 | 42 | {% endblock %} 43 | {% block content %} 44 | 45 |
46 |
47 |

GENDER CLASSIFIER

48 |
49 |
50 |
51 |
52 |
53 |
54 |
55 | 56 | 57 | Person's Height in inches 58 |
59 |
60 | 61 | 62 | Person's Weight in Pounds 63 |
64 | 65 |
66 |
67 |
68 |
69 |
70 | {% if gender_predicted %} 71 |
72 |
73 |
74 |
75 |
76 | {% endif %} 77 |
78 |
79 |
80 |
81 | 82 | 83 | 84 | {% if gender_predicted %} 85 | 108 | {% endif %} 109 | 110 | 111 | 112 | 113 |
114 | 115 | 116 | {% endblock %} 117 | 118 | 119 | 120 | {% block js_scripts %} 121 | 126 | {% endblock %} -------------------------------------------------------------------------------- /templates/boston_house_price_predictor.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | 3 | {% block title %}Boston House Price Prediction{% endblock %} 4 | 5 | {% block css_links %} 6 | 7 | {% endblock %} 8 | 9 | {% block content %} 10 | 11 |
12 |
13 |

BOSTON HOUSE PRICE PREDICTOR

14 |
15 |
16 |
17 |
18 |
19 |
20 |
21 | 22 | 23 | Percentage of Lower Status Population 24 |
25 |
26 | 27 | 28 | Average number of rooms per dwelling 29 |
30 |
31 | 32 | 33 | Pupil Teacher ratio by town 34 |
35 |
36 | 37 | 38 | Proportion of non-retail business acres per town 39 |
40 |
41 | 42 | 43 | Full-value property-tax rate per 10,000 dollars 44 |
45 |
46 | 47 | 48 | Nitric Oxides concentration (parts per 10 million) 49 |
50 | 51 |
52 |
53 |
54 |
55 |
56 |
57 |
58 | 59 | 60 | 61 | {% if price_predicted %} 62 | 85 | {% endif %} 86 | 87 | 88 | 89 | 90 |
91 | 92 | 93 | {% endblock %} 94 | 95 | 96 | 97 | {% block js_scripts %} 98 | 99 | 104 | {% endblock %} -------------------------------------------------------------------------------- /templates/iris_species_classifier.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | 3 | {% block title %}Iris Species Classifier{% endblock %} 4 | 5 | {% block css_links %} 6 | 7 | 8 | {% if iris_predicted %} 9 | 10 | 40 | 41 | {% endif %} 42 | 43 | {% endblock %} 44 | {% block content %} 45 | 46 |
47 |
48 |

IRIS SPECIES CLASSIFIER

49 |
50 |
51 |
52 |
53 |
54 |
55 |
56 | 57 | 58 | Length of the petal of iris flower 59 |
60 |
61 | 62 | 63 | Width of the petal of iris flower 64 |
65 | 66 |
67 |
68 |
69 |
70 |
71 | {% if iris_predicted %} 72 |
73 |
74 |
75 |
76 |
77 | {% endif %} 78 |
79 |
80 |
81 |
82 | 83 | 84 | 85 | {% if iris_predicted %} 86 | 111 | {% endif %} 112 | 113 | 114 | 115 | 116 |
117 | 118 | 119 | {% endblock %} 120 | 121 | 122 | 123 | {% block js_scripts %} 124 | 129 | {% endblock %} -------------------------------------------------------------------------------- /04.Weight_Prediction/README.md: -------------------------------------------------------------------------------- 1 |

Weight Prediction

2 | 3 | ## Table of contents 4 | - [Demo](#demo) 5 | - [Introduction](#introduction) 6 | - [Motivation](#motivation) 7 | - [Project Explanation](#project-explanation) 8 | - [Data Collection](#data-collection) 9 | - [Feature Engineering](#feature-engineering) 10 | - [Data Pre-processing](#data-pre-processing) 11 | - [Feature Observations](#feature-observations) 12 | - [Building Machine Learning Model](#building-machine-learning-model) 13 | - [Model Performance](#model-performance) 14 | - [Conclusion](#conclusion) 15 | 16 | ## Demo 17 | 18 | **App Link** : [Weight prediction](https://ashok-machine-learning-project.herokuapp.com/#weight) 19 | 20 | ![Gender Classifier](../Demos/weight.gif) 21 | 22 | ## Introduction 23 | - This dataset consists of three columns which is Height, Weight and Gender. 24 | - In this project we are going to predict person's weight based on gender and weight. 25 | - Our goal is to train a model with the given data to predict weight. 26 | - Let's look more into the data 27 | 28 |
29 | 30 | ## Motivation 31 | - This is my first end to end data science project. 32 | - I have done this project to stay motivated on Data Science. 33 | - In this project, I have learnt about regression kind of problems. 34 | - I done this project to learn, how the regression works. 35 | 36 | ## Project Explanation 37 | ### Data Collection 38 | - The dataset is collected from [kaggle](https://www.kaggle.com/mustafaali96/weight-height). 39 | - The dataset consists of a CSV file name `weight-height.csv`. 40 | Let's load the collected data. 41 | 42 | ```python 43 | # Loading Dataset 44 | wh = pd.read_csv('../data/weight-height.csv') 45 | ``` 46 | - The dataset consists of 3 features i.e., Height, Weight and Gender. 47 | - The dataset consists of 10,000 samples of different persons. 48 | 49 | ### Feature Engineering 50 | 51 | **1. Height** 52 | 53 | - The heights are mentioned in inches. 54 | - There are no null values in the Height column. 55 | - Let's see the distribution of height data. 56 | 57 | ![Height Distribution](images/height_distribution.png) 58 | 59 | - Here we get a well distributed data and a bell shaped curve shows that it is apt for analysis. 60 | 61 | **2. Weight** 62 | 63 | - The weights are mentioned in pounds. 64 | - There are no null values in the Weight column. 65 | - Let's see the distribution of weight data. 66 | 67 | ![Weight Distribution](images/weight_distribution.png) 68 | 69 | - Here we get a well distributed data and a bell shaped curve shows that it is apt for analysis. 70 | 71 | **3. Gender** 72 | 73 | - Gender is a **nominal** kind of **qualitative data**, because there is no numerical relation between different genders. 74 | - Let's see the relation of Gender with height and weight. 75 | 76 | ![Height Gender](images/gender_height.png) ![Weight Gender](images/gender_weight.png) 77 | 78 | ### Data pre processing 79 | The data which we collect from kaggle is already well pre-processed. So no need of pre-processing. 80 | 81 | ### Feature Observations 82 | Let's see the correlation of features with a heatmap. 83 | 84 | ![Feature Correlation](images/feature-correlaion.png) 85 | ![Feature Correlation](images/feature-relations.png) 86 | 87 | ### Building Machine Learning Model 88 | 89 | ```python 90 | wh['Gender'] = wh['Gender'].apply(lambda x : {'Male' : 1, 'Female' : 0}[x]) 91 | 92 | 93 | X = wh.loc[:, ['Height', 'Gender']].values 94 | y = wh.loc[:, 'Weight'].values 95 | 96 | from sklearn.model_selection import train_test_split 97 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 42) 98 | 99 | from sklearn.linear_model import LinearRegression 100 | from sklearn.pipeline import make_pipeline 101 | from sklearn.preprocessing import StandardScaler 102 | 103 | model = make_pipeline(StandardScaler(), LinearRegression()) 104 | model.fit(X_train, y_train) 105 | ``` 106 | 107 | ```python 108 | # Let's see the training and testing accuracy 109 | print("Training Score : ", model.score(X_train, y_train) * 100) 110 | print("Training Score : ", model.score(X_test, y_test) * 100) 111 | 112 | OUTPUT : 113 | Training Score : 90.28 % 114 | Testing Score : 90.27 % 115 | ``` 116 | 117 | ### Model Performance 118 | 119 | ```python 120 | plt.figure(figsize=(14, 14)) 121 | plt.scatter(y_test, model.predict(X_test), color = 'green') 122 | plt.title('Linear Model Performance', fontsize = 30) 123 | plt.xlabel('Expected Weight', fontsize = 20) 124 | plt.ylabel('Predicted Weight', fontsize = 20) 125 | plt.savefig('../images/modelperformance.png') 126 | plt.show() 127 | ``` 128 | 129 | ![Linear Model Performance](images/modelperformance.png) 130 | 131 | ## Conclusion 132 | - Finally we finished our weight prediction project. 133 | - We have built a Linear Regression Model which performs well with given features and having the Training accuracy of 91.47% and Testing accuracy of 91.90%. 134 | 135 | ##### If you love my work, kindly give a Star to my repository. 136 | ##### Facing any issues, feel free to post your issues on my GitHub. 137 | ##### Thank you Everyone for seeing my work. 138 | -------------------------------------------------------------------------------- /03.Gender_Classifcation/README.md: -------------------------------------------------------------------------------- 1 |

Gender Classification

2 | 3 | ## Table of contents 4 | - [Demo](#demo) 5 | - [Introduction](#introduction) 6 | - [Motivation](#motivation) 7 | - [Project Explanation](#project-explanation) 8 | - [Data Collection](#data-collection) 9 | - [Feature Engineering](#feature-engineering) 10 | - [Data Pre-processing](#data-pre-processing) 11 | - [Feature Observations](#feature-observations) 12 | - [Building Machine Learning Model](#building-machine-learning-model) 13 | - [Model Performance](#model-performance) 14 | - [Conclusion](#conclusion) 15 | 16 | ## Demo 17 | 18 | **App Link** : [Gender Classification](https://ashok-machine-learning-project.herokuapp.com/#gender) 19 | 20 | ![Gender Classifier](../Demos/gender.gif) 21 | 22 | ## Introduction 23 | - This dataset consists of three columns which is Height, Weight and Gender. 24 | - In this project we are going to classify person's gender based on his/her height and weight. 25 | - Our goal is to train a model with the given data to classify gender. 26 | - Let's look more into the data 27 | 28 |
29 | 30 | ## Motivation 31 | - This is my second end to end data science project. 32 | - I have done this project to stay motivated on Data Science. 33 | - In this project, I have learnt about classification kind of problems. 34 | - I done this project to learn, how the classification works. 35 | 36 | ## Project Explanation 37 | ### Data Collection 38 | - The dataset is collected from [kaggle](https://www.kaggle.com/mustafaali96/weight-height). 39 | - The dataset consists of a CSV file name `weight-height.csv`. 40 | Let's load the collected data. 41 | 42 | ```python 43 | # Loading Dataset 44 | wh = pd.read_csv('../data/weight-height.csv') 45 | ``` 46 | - The dataset consists of 3 features i.e., Height, Weight and Gender. 47 | - The dataset consists of 10,000 samples of different persons. 48 | 49 | ### Feature Engineering 50 | 51 | **1. Height** 52 | 53 | - The heights are mentioned in inches. 54 | - There are no null values in the Height column. 55 | - Let's see the distribution of height data. 56 | 57 | ![Height Distribution](images/height_distribution.png) 58 | 59 | - Here we get a well distributed data and a bell shaped curve shows that it is apt for analysis. 60 | 61 | **2. Weight** 62 | 63 | - The weights are mentioned in pounds. 64 | - There are no null values in the Weight column. 65 | - Let's see the distribution of weight data. 66 | 67 | ![Weight Distribution](images/weight_distribution.png) 68 | 69 | - Here we get a well distributed data and a bell shaped curve shows that it is apt for analysis. 70 | 71 | **3. Gender** 72 | 73 | - Gender is a **nominal** kind of **qualitative data**, because there is no numerical relation between different genders. 74 | - Let's see the relation of Gender with height and weight. 75 | 76 | ![Height Gender](images/gender_height.png) ![Weight Distribution](images/gender_weight.png) 77 | 78 | ### Data pre processing 79 | The data which we collect from kaggle is already well pre-processed. So no need of pre-processing. 80 | 81 | ### Feature Observations 82 | Let's see the correlation of features with a heatmap. 83 | 84 | ![Feature Correlation](images/feature-correlaion.png) 85 | ![Feature Correlation](images/feature-relations.png) 86 | 87 | ### Building Machine Learning Model 88 | 89 | ```python 90 | from sklearn.pipeline import make_pipeline 91 | from sklearn.preprocessing import StandardScaler 92 | 93 | X = wh.iloc[:, [1, 2]].values 94 | y = wh.iloc[:, 0].values 95 | 96 | from sklearn.model_selection import train_test_split 97 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42) 98 | 99 | from sklearn.svm import SVC 100 | clf = make_pipeline(StandardScaler(), SVC(gamma='auto', probability = True)) 101 | clf.fit(X_train, y_train) 102 | ``` 103 | 104 | ```python 105 | # Let's see the training and testing accuracy 106 | print("Training Score : ", clf.score(X_train, y_train) * 100) 107 | print("Training Score : ", clf.score(X_test, y_test) * 100) 108 | 109 | OUTPUT : 110 | Training Score : 91.47 % 111 | Testing Score : 91.90 % 112 | ``` 113 | 114 | ### Model Performance 115 | 116 | ```python 117 | # Let's see the classification report 118 | from sklearn.metrics import classification_report 119 | print(classification_report(y_test, clf.predict(X_test))) 120 | 121 | REPORT : 122 | precision recall f1-score support 123 | 124 | Female 0.92 0.91 0.92 1463 125 | Male 0.92 0.92 0.92 1537 126 | 127 | accuracy 0.92 3000 128 | macro avg 0.92 0.92 0.92 3000 129 | weighted avg 0.92 0.92 0.92 3000 130 | ``` 131 | 132 | ## Conclusion 133 | - Finally we finished our gender classification project. 134 | - We have built a Support Vector Machine Classifier which performs well with given features and having the Training accuracy of 91.47% and Testing accuracy of 91.90%. 135 | 136 | ##### If you love my work, kindly give a Star to my repository. 137 | ##### Facing any issues, feel free to post your issues on my GitHub. 138 | ##### Thank you Everyone for seeing my work. 139 | -------------------------------------------------------------------------------- /templates/base.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | {% block css_links %}{% endblock %} 14 | {% block title %}{% endblock %} 15 | 16 | 17 | 18 | 19 | 58 | 59 | 60 | {% block content %}{% endblock %} 61 | 62 | 63 | 64 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | {% block js_scripts %}{% endblock %} 124 | 125 | -------------------------------------------------------------------------------- /templates/loan_status_predictor.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | 3 | {% block title %}Loan Status Predictor{% endblock %} 4 | 5 | {% block css_links %} 6 | 7 | 8 | {% if status_predicted %} 9 | 10 | 39 | 40 | {% endif %} 41 | 42 | {% endblock %} 43 | {% block content %} 44 | 45 |
46 |
47 |

LOAN STATUS PREDICTOR

48 |
49 |
50 |
51 |
52 |
53 |
54 |
55 | 56 | 60 | Applicant's Previous Credit History 61 |
62 |
63 | 64 | 65 | Loan Amount asked by the applicant 66 |
67 |
68 | 69 | 70 | Applicants's present Income in dollars ($) 71 |
72 |
73 | 74 | 75 | Co-applicants's present Income in dollars ($) 76 |
77 |
78 | 79 | 80 | No. of Children 81 |
82 | 83 |
84 |
85 |
86 |
87 |
88 | {% if status_predicted %} 89 |
90 |
91 |
92 |
93 |
94 | {% endif %} 95 |
96 |
97 |
98 |
99 | 100 | 101 | 102 | {% if status_predicted %} 103 | 127 | {% endif %} 128 | 129 | 130 | 131 | 132 |
133 | 134 | 135 | {% endblock %} 136 | 137 | 138 | 139 | {% block js_scripts %} 140 | 145 | {% endblock %} -------------------------------------------------------------------------------- /01.Iris_Species_Classification/data/Iris.csv: -------------------------------------------------------------------------------- 1 | Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species 2 | 1,5.1,3.5,1.4,0.2,Iris-setosa 3 | 2,4.9,3.0,1.4,0.2,Iris-setosa 4 | 3,4.7,3.2,1.3,0.2,Iris-setosa 5 | 4,4.6,3.1,1.5,0.2,Iris-setosa 6 | 5,5.0,3.6,1.4,0.2,Iris-setosa 7 | 6,5.4,3.9,1.7,0.4,Iris-setosa 8 | 7,4.6,3.4,1.4,0.3,Iris-setosa 9 | 8,5.0,3.4,1.5,0.2,Iris-setosa 10 | 9,4.4,2.9,1.4,0.2,Iris-setosa 11 | 10,4.9,3.1,1.5,0.1,Iris-setosa 12 | 11,5.4,3.7,1.5,0.2,Iris-setosa 13 | 12,4.8,3.4,1.6,0.2,Iris-setosa 14 | 13,4.8,3.0,1.4,0.1,Iris-setosa 15 | 14,4.3,3.0,1.1,0.1,Iris-setosa 16 | 15,5.8,4.0,1.2,0.2,Iris-setosa 17 | 16,5.7,4.4,1.5,0.4,Iris-setosa 18 | 17,5.4,3.9,1.3,0.4,Iris-setosa 19 | 18,5.1,3.5,1.4,0.3,Iris-setosa 20 | 19,5.7,3.8,1.7,0.3,Iris-setosa 21 | 20,5.1,3.8,1.5,0.3,Iris-setosa 22 | 21,5.4,3.4,1.7,0.2,Iris-setosa 23 | 22,5.1,3.7,1.5,0.4,Iris-setosa 24 | 23,4.6,3.6,1.0,0.2,Iris-setosa 25 | 24,5.1,3.3,1.7,0.5,Iris-setosa 26 | 25,4.8,3.4,1.9,0.2,Iris-setosa 27 | 26,5.0,3.0,1.6,0.2,Iris-setosa 28 | 27,5.0,3.4,1.6,0.4,Iris-setosa 29 | 28,5.2,3.5,1.5,0.2,Iris-setosa 30 | 29,5.2,3.4,1.4,0.2,Iris-setosa 31 | 30,4.7,3.2,1.6,0.2,Iris-setosa 32 | 31,4.8,3.1,1.6,0.2,Iris-setosa 33 | 32,5.4,3.4,1.5,0.4,Iris-setosa 34 | 33,5.2,4.1,1.5,0.1,Iris-setosa 35 | 34,5.5,4.2,1.4,0.2,Iris-setosa 36 | 35,4.9,3.1,1.5,0.1,Iris-setosa 37 | 36,5.0,3.2,1.2,0.2,Iris-setosa 38 | 37,5.5,3.5,1.3,0.2,Iris-setosa 39 | 38,4.9,3.1,1.5,0.1,Iris-setosa 40 | 39,4.4,3.0,1.3,0.2,Iris-setosa 41 | 40,5.1,3.4,1.5,0.2,Iris-setosa 42 | 41,5.0,3.5,1.3,0.3,Iris-setosa 43 | 42,4.5,2.3,1.3,0.3,Iris-setosa 44 | 43,4.4,3.2,1.3,0.2,Iris-setosa 45 | 44,5.0,3.5,1.6,0.6,Iris-setosa 46 | 45,5.1,3.8,1.9,0.4,Iris-setosa 47 | 46,4.8,3.0,1.4,0.3,Iris-setosa 48 | 47,5.1,3.8,1.6,0.2,Iris-setosa 49 | 48,4.6,3.2,1.4,0.2,Iris-setosa 50 | 49,5.3,3.7,1.5,0.2,Iris-setosa 51 | 50,5.0,3.3,1.4,0.2,Iris-setosa 52 | 51,7.0,3.2,4.7,1.4,Iris-versicolor 53 | 52,6.4,3.2,4.5,1.5,Iris-versicolor 54 | 53,6.9,3.1,4.9,1.5,Iris-versicolor 55 | 54,5.5,2.3,4.0,1.3,Iris-versicolor 56 | 55,6.5,2.8,4.6,1.5,Iris-versicolor 57 | 56,5.7,2.8,4.5,1.3,Iris-versicolor 58 | 57,6.3,3.3,4.7,1.6,Iris-versicolor 59 | 58,4.9,2.4,3.3,1.0,Iris-versicolor 60 | 59,6.6,2.9,4.6,1.3,Iris-versicolor 61 | 60,5.2,2.7,3.9,1.4,Iris-versicolor 62 | 61,5.0,2.0,3.5,1.0,Iris-versicolor 63 | 62,5.9,3.0,4.2,1.5,Iris-versicolor 64 | 63,6.0,2.2,4.0,1.0,Iris-versicolor 65 | 64,6.1,2.9,4.7,1.4,Iris-versicolor 66 | 65,5.6,2.9,3.6,1.3,Iris-versicolor 67 | 66,6.7,3.1,4.4,1.4,Iris-versicolor 68 | 67,5.6,3.0,4.5,1.5,Iris-versicolor 69 | 68,5.8,2.7,4.1,1.0,Iris-versicolor 70 | 69,6.2,2.2,4.5,1.5,Iris-versicolor 71 | 70,5.6,2.5,3.9,1.1,Iris-versicolor 72 | 71,5.9,3.2,4.8,1.8,Iris-versicolor 73 | 72,6.1,2.8,4.0,1.3,Iris-versicolor 74 | 73,6.3,2.5,4.9,1.5,Iris-versicolor 75 | 74,6.1,2.8,4.7,1.2,Iris-versicolor 76 | 75,6.4,2.9,4.3,1.3,Iris-versicolor 77 | 76,6.6,3.0,4.4,1.4,Iris-versicolor 78 | 77,6.8,2.8,4.8,1.4,Iris-versicolor 79 | 78,6.7,3.0,5.0,1.7,Iris-versicolor 80 | 79,6.0,2.9,4.5,1.5,Iris-versicolor 81 | 80,5.7,2.6,3.5,1.0,Iris-versicolor 82 | 81,5.5,2.4,3.8,1.1,Iris-versicolor 83 | 82,5.5,2.4,3.7,1.0,Iris-versicolor 84 | 83,5.8,2.7,3.9,1.2,Iris-versicolor 85 | 84,6.0,2.7,5.1,1.6,Iris-versicolor 86 | 85,5.4,3.0,4.5,1.5,Iris-versicolor 87 | 86,6.0,3.4,4.5,1.6,Iris-versicolor 88 | 87,6.7,3.1,4.7,1.5,Iris-versicolor 89 | 88,6.3,2.3,4.4,1.3,Iris-versicolor 90 | 89,5.6,3.0,4.1,1.3,Iris-versicolor 91 | 90,5.5,2.5,4.0,1.3,Iris-versicolor 92 | 91,5.5,2.6,4.4,1.2,Iris-versicolor 93 | 92,6.1,3.0,4.6,1.4,Iris-versicolor 94 | 93,5.8,2.6,4.0,1.2,Iris-versicolor 95 | 94,5.0,2.3,3.3,1.0,Iris-versicolor 96 | 95,5.6,2.7,4.2,1.3,Iris-versicolor 97 | 96,5.7,3.0,4.2,1.2,Iris-versicolor 98 | 97,5.7,2.9,4.2,1.3,Iris-versicolor 99 | 98,6.2,2.9,4.3,1.3,Iris-versicolor 100 | 99,5.1,2.5,3.0,1.1,Iris-versicolor 101 | 100,5.7,2.8,4.1,1.3,Iris-versicolor 102 | 101,6.3,3.3,6.0,2.5,Iris-virginica 103 | 102,5.8,2.7,5.1,1.9,Iris-virginica 104 | 103,7.1,3.0,5.9,2.1,Iris-virginica 105 | 104,6.3,2.9,5.6,1.8,Iris-virginica 106 | 105,6.5,3.0,5.8,2.2,Iris-virginica 107 | 106,7.6,3.0,6.6,2.1,Iris-virginica 108 | 107,4.9,2.5,4.5,1.7,Iris-virginica 109 | 108,7.3,2.9,6.3,1.8,Iris-virginica 110 | 109,6.7,2.5,5.8,1.8,Iris-virginica 111 | 110,7.2,3.6,6.1,2.5,Iris-virginica 112 | 111,6.5,3.2,5.1,2.0,Iris-virginica 113 | 112,6.4,2.7,5.3,1.9,Iris-virginica 114 | 113,6.8,3.0,5.5,2.1,Iris-virginica 115 | 114,5.7,2.5,5.0,2.0,Iris-virginica 116 | 115,5.8,2.8,5.1,2.4,Iris-virginica 117 | 116,6.4,3.2,5.3,2.3,Iris-virginica 118 | 117,6.5,3.0,5.5,1.8,Iris-virginica 119 | 118,7.7,3.8,6.7,2.2,Iris-virginica 120 | 119,7.7,2.6,6.9,2.3,Iris-virginica 121 | 120,6.0,2.2,5.0,1.5,Iris-virginica 122 | 121,6.9,3.2,5.7,2.3,Iris-virginica 123 | 122,5.6,2.8,4.9,2.0,Iris-virginica 124 | 123,7.7,2.8,6.7,2.0,Iris-virginica 125 | 124,6.3,2.7,4.9,1.8,Iris-virginica 126 | 125,6.7,3.3,5.7,2.1,Iris-virginica 127 | 126,7.2,3.2,6.0,1.8,Iris-virginica 128 | 127,6.2,2.8,4.8,1.8,Iris-virginica 129 | 128,6.1,3.0,4.9,1.8,Iris-virginica 130 | 129,6.4,2.8,5.6,2.1,Iris-virginica 131 | 130,7.2,3.0,5.8,1.6,Iris-virginica 132 | 131,7.4,2.8,6.1,1.9,Iris-virginica 133 | 132,7.9,3.8,6.4,2.0,Iris-virginica 134 | 133,6.4,2.8,5.6,2.2,Iris-virginica 135 | 134,6.3,2.8,5.1,1.5,Iris-virginica 136 | 135,6.1,2.6,5.6,1.4,Iris-virginica 137 | 136,7.7,3.0,6.1,2.3,Iris-virginica 138 | 137,6.3,3.4,5.6,2.4,Iris-virginica 139 | 138,6.4,3.1,5.5,1.8,Iris-virginica 140 | 139,6.0,3.0,4.8,1.8,Iris-virginica 141 | 140,6.9,3.1,5.4,2.1,Iris-virginica 142 | 141,6.7,3.1,5.6,2.4,Iris-virginica 143 | 142,6.9,3.1,5.1,2.3,Iris-virginica 144 | 143,5.8,2.7,5.1,1.9,Iris-virginica 145 | 144,6.8,3.2,5.9,2.3,Iris-virginica 146 | 145,6.7,3.3,5.7,2.5,Iris-virginica 147 | 146,6.7,3.0,5.2,2.3,Iris-virginica 148 | 147,6.3,2.5,5.0,1.9,Iris-virginica 149 | 148,6.5,3.0,5.2,2.0,Iris-virginica 150 | 149,6.2,3.4,5.4,2.3,Iris-virginica 151 | 150,5.9,3.0,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /data/ML_Projects.csv: -------------------------------------------------------------------------------- 1 | Project_No,Project_Name,Start_Date,End_Date,Description,Motivation,front_image,Algorithms_Used,Algorithms_link,python_libraries,Frameworks_used,Technologies/Languages,Tools/IDE,Training Score,Test Score,modal_control,modal_name,url,GitHub Link,Blog Link,id 2 | 1,Weight Prediction,12-Jan-2020,17-Jan-2020,"In this project we will predict a person's weight based on height and gender. But with height and gender alone we can't decide weight. There are some other factors such as age, type of work, etc..,","This project was started as a motivation for learning Machine Learning Algorithms and to learn the different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.",images/photos/weight.png,Linear_Regression,http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html,images/photos/sklearn.png|images/photos/numpy.png|images/photos/pandas.png|images/photos/matplotlib.jpg,images/photos/flask.png,images/photos/python.png|images/photos/anaconda.png,images/photos/sublime.png|images/photos/vs_code.png,85.74,90.28,#weight_pred,weight_pred,weight_predictor,https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/04.Weight_Prediction,https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/04.Weight_Prediction,weight 3 | 2,Gender Classification,18-Jan-2020,31-Jan-2020,"In this project we will classify a person's gender based on height and weight. But only with height and weight itself we can't predict the gender. Since this was my beginner level project, I am going to predict person's gender based on height and weight only.","This project was started as a motivation for learning Machine Learning Algorithms and to learn the different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.",images/photos/gender.png,Support_Vector_Classification,http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html,images/photos/sklearn.png|images/photos/numpy.png|images/photos/pandas.png|images/photos/matplotlib.jpg,images/photos/flask.png,images/photos/python.png|images/photos/anaconda.png,images/photos/sublime.png|images/photos/vs_code.png,91.47,91.9,#gender_classifier,gender_classifier,gender_classifier,https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/03.Gender_Classifcation,https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/03.Gender_Classifcation,gender 4 | 3,Iris Species Classification,27-Jun-2020,30-Jun-2020,"In this project we will classify the Iris species based on their features such as Sepal length, Sepal width, Petal length and Petal width.","This project was started as a motivation for learning Machine Learning Algorithms and to learn the different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.",images/photos/iris.jpg,Decision_Tree_Classifier,https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html,images/photos/sklearn.png|images/photos/numpy.png|images/photos/pandas.png|images/photos/matplotlib.jpg|images/photos/seaborn.png,images/photos/flask.png,images/photos/python.png|images/photos/anaconda.png,images/photos/sublime.png|images/photos/vs_code.png,99.11,100,#iris_species,iris_species,iris_species_classifier,https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/01.Iris_Species_Classification,https://medium.com/analytics-vidhya/iris-species-classification-machine-learning-model-8d7fa4e48f81?source=your_stories_page---------------------------,iris 5 | 4,Loan Status Prediction,1-Jul-2020,7-Jul-2020,"In this project, we are going to classify an individual whether he/she can get the loan amount based on his/her Income, Education, Working Experience, Loan which is taken previously, and many more factors.","This project was started as a motivation for learning Machine Learning Algorithms and learning the different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model along with visualization of model performance.",images/photos/loan-approved.jpg,Decision_Tree_Classifier,https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html,images/photos/sklearn.png|images/photos/numpy.png|images/photos/pandas.png|images/photos/matplotlib.jpg|images/photos/seaborn.png,images/photos/flask.png,images/photos/python.png|images/photos/anaconda.png,images/photos/sublime.png|images/photos/vs_code.png,85.36,85.54,#loan_status,loan_status,loan_status_predictor,https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/02.Loan_Status_Prediction,https://medium.com/analytics-vidhya/loan-status-prediction-machine-learning-model-ca703246c7ec?source=your_stories_page---------------------------,loan 6 | 5,Boston House Price Prediction,10-Jul-2020,11-Jul-2020,"In this project, we are going to predict boston house prices based on some important features such as lower status population percentage (LSTAT), number of rooms (RM), pupil-teacher ratio by town (PTRATIO), proportion of non-retail business acres per town (INDUS), full-value property-tax rate per 10,000 dollars (TAX), nitric oxides concentration (NOX), etc..,","This project was started as a motivation for learning Machine Learning Algorithms and learning the different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model along with visualization of model performance using ensemble learning.",images/photos/house.jpg,Random_Forest_Regressor,http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html,images/photos/sklearn.png|images/photos/numpy.png|images/photos/pandas.png|images/photos/matplotlib.jpg|images/photos/seaborn.png,images/photos/flask.png,images/photos/python.png|images/photos/anaconda.png,images/photos/sublime.png|images/photos/vs_code.png,98.43,96.15,#boston_price,boston_price,boston_house_price_predictor,https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/05.Boston_House_Price_Prediction,https://medium.com/@Ashok_kumar_K/boston-housing-price-prediction-e279074f356c?source=your_stories_page---------------------------,boston -------------------------------------------------------------------------------- /static/css/bootstrap-4.5.0/bootstrap-reboot.css: -------------------------------------------------------------------------------- 1 | /*! 2 | * Bootstrap Reboot v4.5.0 (https://getbootstrap.com/) 3 | * Copyright 2011-2020 The Bootstrap Authors 4 | * Copyright 2011-2020 Twitter, Inc. 5 | * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) 6 | * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md) 7 | */ 8 | *, 9 | *::before, 10 | *::after { 11 | box-sizing: border-box; 12 | } 13 | 14 | html { 15 | font-family: sans-serif; 16 | line-height: 1.15; 17 | -webkit-text-size-adjust: 100%; 18 | -webkit-tap-highlight-color: rgba(0, 0, 0, 0); 19 | } 20 | 21 | article, aside, figcaption, figure, footer, header, hgroup, main, nav, section { 22 | display: block; 23 | } 24 | 25 | body { 26 | margin: 0; 27 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; 28 | font-size: 1rem; 29 | font-weight: 400; 30 | line-height: 1.5; 31 | color: #212529; 32 | text-align: left; 33 | background-color: #fff; 34 | } 35 | 36 | [tabindex="-1"]:focus:not(:focus-visible) { 37 | outline: 0 !important; 38 | } 39 | 40 | hr { 41 | box-sizing: content-box; 42 | height: 0; 43 | overflow: visible; 44 | } 45 | 46 | h1, h2, h3, h4, h5, h6 { 47 | margin-top: 0; 48 | margin-bottom: 0.5rem; 49 | } 50 | 51 | p { 52 | margin-top: 0; 53 | margin-bottom: 1rem; 54 | } 55 | 56 | abbr[title], 57 | abbr[data-original-title] { 58 | text-decoration: underline; 59 | -webkit-text-decoration: underline dotted; 60 | text-decoration: underline dotted; 61 | cursor: help; 62 | border-bottom: 0; 63 | -webkit-text-decoration-skip-ink: none; 64 | text-decoration-skip-ink: none; 65 | } 66 | 67 | address { 68 | margin-bottom: 1rem; 69 | font-style: normal; 70 | line-height: inherit; 71 | } 72 | 73 | ol, 74 | ul, 75 | dl { 76 | margin-top: 0; 77 | margin-bottom: 1rem; 78 | } 79 | 80 | ol ol, 81 | ul ul, 82 | ol ul, 83 | ul ol { 84 | margin-bottom: 0; 85 | } 86 | 87 | dt { 88 | font-weight: 700; 89 | } 90 | 91 | dd { 92 | margin-bottom: .5rem; 93 | margin-left: 0; 94 | } 95 | 96 | blockquote { 97 | margin: 0 0 1rem; 98 | } 99 | 100 | b, 101 | strong { 102 | font-weight: bolder; 103 | } 104 | 105 | small { 106 | font-size: 80%; 107 | } 108 | 109 | sub, 110 | sup { 111 | position: relative; 112 | font-size: 75%; 113 | line-height: 0; 114 | vertical-align: baseline; 115 | } 116 | 117 | sub { 118 | bottom: -.25em; 119 | } 120 | 121 | sup { 122 | top: -.5em; 123 | } 124 | 125 | a { 126 | color: #007bff; 127 | text-decoration: none; 128 | background-color: transparent; 129 | } 130 | 131 | a:hover { 132 | color: #0056b3; 133 | text-decoration: underline; 134 | } 135 | 136 | a:not([href]) { 137 | color: inherit; 138 | text-decoration: none; 139 | } 140 | 141 | a:not([href]):hover { 142 | color: inherit; 143 | text-decoration: none; 144 | } 145 | 146 | pre, 147 | code, 148 | kbd, 149 | samp { 150 | font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; 151 | font-size: 1em; 152 | } 153 | 154 | pre { 155 | margin-top: 0; 156 | margin-bottom: 1rem; 157 | overflow: auto; 158 | -ms-overflow-style: scrollbar; 159 | } 160 | 161 | figure { 162 | margin: 0 0 1rem; 163 | } 164 | 165 | img { 166 | vertical-align: middle; 167 | border-style: none; 168 | } 169 | 170 | svg { 171 | overflow: hidden; 172 | vertical-align: middle; 173 | } 174 | 175 | table { 176 | border-collapse: collapse; 177 | } 178 | 179 | caption { 180 | padding-top: 0.75rem; 181 | padding-bottom: 0.75rem; 182 | color: #6c757d; 183 | text-align: left; 184 | caption-side: bottom; 185 | } 186 | 187 | th { 188 | text-align: inherit; 189 | } 190 | 191 | label { 192 | display: inline-block; 193 | margin-bottom: 0.5rem; 194 | } 195 | 196 | button { 197 | border-radius: 0; 198 | } 199 | 200 | button:focus { 201 | outline: 1px dotted; 202 | outline: 5px auto -webkit-focus-ring-color; 203 | } 204 | 205 | input, 206 | button, 207 | select, 208 | optgroup, 209 | textarea { 210 | margin: 0; 211 | font-family: inherit; 212 | font-size: inherit; 213 | line-height: inherit; 214 | } 215 | 216 | button, 217 | input { 218 | overflow: visible; 219 | } 220 | 221 | button, 222 | select { 223 | text-transform: none; 224 | } 225 | 226 | [role="button"] { 227 | cursor: pointer; 228 | } 229 | 230 | select { 231 | word-wrap: normal; 232 | } 233 | 234 | button, 235 | [type="button"], 236 | [type="reset"], 237 | [type="submit"] { 238 | -webkit-appearance: button; 239 | } 240 | 241 | button:not(:disabled), 242 | [type="button"]:not(:disabled), 243 | [type="reset"]:not(:disabled), 244 | [type="submit"]:not(:disabled) { 245 | cursor: pointer; 246 | } 247 | 248 | button::-moz-focus-inner, 249 | [type="button"]::-moz-focus-inner, 250 | [type="reset"]::-moz-focus-inner, 251 | [type="submit"]::-moz-focus-inner { 252 | padding: 0; 253 | border-style: none; 254 | } 255 | 256 | input[type="radio"], 257 | input[type="checkbox"] { 258 | box-sizing: border-box; 259 | padding: 0; 260 | } 261 | 262 | textarea { 263 | overflow: auto; 264 | resize: vertical; 265 | } 266 | 267 | fieldset { 268 | min-width: 0; 269 | padding: 0; 270 | margin: 0; 271 | border: 0; 272 | } 273 | 274 | legend { 275 | display: block; 276 | width: 100%; 277 | max-width: 100%; 278 | padding: 0; 279 | margin-bottom: .5rem; 280 | font-size: 1.5rem; 281 | line-height: inherit; 282 | color: inherit; 283 | white-space: normal; 284 | } 285 | 286 | progress { 287 | vertical-align: baseline; 288 | } 289 | 290 | [type="number"]::-webkit-inner-spin-button, 291 | [type="number"]::-webkit-outer-spin-button { 292 | height: auto; 293 | } 294 | 295 | [type="search"] { 296 | outline-offset: -2px; 297 | -webkit-appearance: none; 298 | } 299 | 300 | [type="search"]::-webkit-search-decoration { 301 | -webkit-appearance: none; 302 | } 303 | 304 | ::-webkit-file-upload-button { 305 | font: inherit; 306 | -webkit-appearance: button; 307 | } 308 | 309 | output { 310 | display: inline-block; 311 | } 312 | 313 | summary { 314 | display: list-item; 315 | cursor: pointer; 316 | } 317 | 318 | template { 319 | display: none; 320 | } 321 | 322 | [hidden] { 323 | display: none !important; 324 | } 325 | /*# sourceMappingURL=bootstrap-reboot.css.map */ -------------------------------------------------------------------------------- /templates/all_projects.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | 3 | {% block title %}Machine Learning Projects{% endblock %} 4 | 5 | {% block css_links %} 6 | 7 | {% endblock %} 8 | {% block content %} 9 | 10 |
11 | {% for row in range(rows) %} 12 |
13 | {% for col in range(3) %} 14 | 15 | {% if ml_projects_data[0][row*3 + col] %} 16 |
17 |
18 |

{{ ml_projects_data[1][row*3 + col] }}

19 |
20 |
21 | ... 22 |
23 | 26 |
27 | 28 | 29 | 162 |
163 |
164 |
165 |
166 | {% endif %} 167 | 168 | {% endfor %} 169 |
170 | {% endfor %} 171 | 172 |
173 | 174 | 175 | {% endblock %} -------------------------------------------------------------------------------- /ml_projects.py: -------------------------------------------------------------------------------- 1 | # Importing Flask essentials 2 | from flask import Flask, request, render_template, redirect, url_for 3 | 4 | # Importing Pandas for Data Reading 5 | import pandas as pd 6 | 7 | # Importing Numpy for Array Manipulation 8 | import numpy as np 9 | 10 | # Importing own modules 11 | from py_modules import data_extraction as de 12 | 13 | # Import pickle module to load models 14 | import pickle 15 | 16 | # Flask App 17 | ml_app = Flask(__name__) 18 | 19 | # Reading my social links data 20 | social = pd.read_csv('data/social-media-links.csv') 21 | 22 | # Extraction of social link data 23 | social_data = de.get_social_link_data(social) 24 | 25 | # Counting number of Data 26 | number_of_links = len(social_data[0]) 27 | 28 | # Root or Home page - About Me 29 | @ml_app.route('/') 30 | def all_projects(): 31 | # Reading ML_Projects data 32 | ml_projects = pd.read_csv('data/ML_Projects.csv') 33 | # Calculating number of projects 34 | number_of_projects = ml_projects.shape[0] 35 | # Calculating rows for UI 36 | rows = (number_of_projects // 3) + 1 37 | # Extracting Machine Learning Projects Data 38 | ml_projects_data = de.get_ml_projects_data(ml_projects) 39 | 40 | # Extracting Libraries links 41 | libraries = ml_projects_data[9] 42 | libraries_used = [[str(library) for library in rows.split('|')] for rows in libraries] 43 | 44 | # Extracting Technologies 45 | technologies = ml_projects_data[11] 46 | technologies_used = [[str(technology) for technology in rows.split('|')] for rows in technologies] 47 | 48 | # Extracting Frame works 49 | frameworks = ml_projects_data[10] 50 | frameworks_used = [[str(framework) for framework in rows.split('|')] for rows in frameworks] 51 | 52 | # Extracting Tools / IDE 53 | tools = ml_projects_data[12] 54 | tools_used = [[str(tool) for tool in rows.split('|')] for rows in tools] 55 | 56 | return render_template('all_projects.html', 57 | social_data=social_data, 58 | n = number_of_links, 59 | ml_projects_data = ml_projects_data, 60 | rows = rows, 61 | libraries_used = libraries_used, 62 | technologies_used = technologies_used, 63 | frameworks_used = frameworks_used, 64 | tools_used = tools_used) 65 | 66 | 67 | # Boston House Price Predictor 68 | @ml_app.route('/boston_house_price_predictor', methods = ['POST', 'GET']) 69 | def boston_house_price_predictor(): 70 | 71 | if request.method == "POST": 72 | # Getting Input Data from UI 73 | 'LSTAT', 'RM', 'PTRATIO', 'INDUS', 'TAX', 'NOX' 74 | lstat = float(request.form['lstat']) 75 | rm = float(request.form['rm']) 76 | ptratio = float(request.form['ptratio']) 77 | indus = float(request.form['indus']) 78 | tax = float(request.form['tax']) 79 | nox = float(request.form['nox']) 80 | 81 | # Forming an input array 82 | input_array = [[lstat, rm, ptratio, indus, tax, nox]] 83 | 84 | # Loading Loan Status Predictor Model 85 | boston_house_price_predictor = pickle.load(open('models/boston_house_price_predictor.pkl', 'rb')) 86 | 87 | # Prediction 88 | price_predicted = np.round(boston_house_price_predictor.predict(input_array), 2) 89 | 90 | # Predicting Probability 91 | # predict_proba = boston_house_price_predictor.predict_proba(input_array) * 100 92 | return render_template('boston_house_price_predictor.html', 93 | social_data = social_data, 94 | n = number_of_links, 95 | price_predicted = price_predicted, 96 | input_array = input_array) 97 | return render_template('boston_house_price_predictor.html', 98 | social_data = social_data, 99 | n = number_of_links) 100 | 101 | 102 | 103 | # Loan Status Predictor 104 | @ml_app.route('/loan_status_predictor', methods = ['POST', 'GET']) 105 | def loan_status_predictor(): 106 | 107 | if request.method == "POST": 108 | # Getting Input Data from UI 109 | credit_history = float(request.form['credit_history']) 110 | loan_amount = float(request.form['loan_amount']) 111 | applicant_income = float(request.form['applicant_income']) 112 | coapplicant_income = float(request.form['coapplicant_income']) 113 | dependents = float(request.form['dependents']) 114 | 115 | # Forming an input array 116 | input_array = [[credit_history, loan_amount, applicant_income, coapplicant_income, dependents]] 117 | 118 | # Loading Loan Status Predictor Model 119 | loan_status_predictor_model = pickle.load(open('models/loan_status_predictor.pkl', 'rb')) 120 | 121 | # Prediction 122 | status_predicted = loan_status_predictor_model.predict(input_array) 123 | 124 | # Predicting Probability 125 | predict_proba = loan_status_predictor_model.predict_proba(input_array) * 100 126 | return render_template('loan_status_predictor.html', 127 | social_data = social_data, 128 | n = number_of_links, 129 | status_predicted = status_predicted, 130 | predict_proba = predict_proba) 131 | return render_template('loan_status_predictor.html', 132 | social_data = social_data, 133 | n = number_of_links) 134 | 135 | 136 | 137 | # Iris Species Classifier 138 | @ml_app.route('/iris_species_classifier', methods = ['POST', 'GET']) 139 | def iris_species_classifier(): 140 | if request.method == 'POST': 141 | # Getting Data from UI 142 | petal_length = float(request.form['petal_length']) 143 | petal_width = float(request.form['petal_width']) 144 | 145 | # Forming an Input Array 146 | input_array = [[petal_length, petal_width]] 147 | 148 | # Loading Iris Species Classifier Model 149 | iris_species_classifier_model = pickle.load(open('models/iris_species_classifier.pkl', 'rb')) 150 | 151 | # Prediction 152 | iris_predicted = iris_species_classifier_model.predict(input_array) 153 | 154 | # Predicting Probaility 155 | predict_proba = iris_species_classifier_model.predict_proba(input_array) * 100 156 | 157 | return render_template('iris_species_classifier.html', 158 | social_data = social_data, 159 | n = number_of_links, 160 | iris_predicted = iris_predicted, 161 | predict_proba = predict_proba) 162 | return render_template('iris_species_classifier.html', 163 | social_data = social_data, 164 | n = number_of_links) 165 | 166 | 167 | 168 | # Gender Classifier 169 | @ml_app.route('/gender_classifier', methods = ['POST', 'GET']) 170 | def gender_classifier(): 171 | if request.method == 'POST': 172 | # Getting Data from UI 173 | height = float(request.form['height']) 174 | weight = float(request.form['weight']) 175 | 176 | # Forming an Input Array 177 | input_array = [[height, weight]] 178 | 179 | # Loading Gender Classifier Model 180 | gender_classifier_model = pickle.load(open('models/gender_classifier.pkl', 'rb')) 181 | 182 | # Prediction 183 | gender_predicted = gender_classifier_model.predict(input_array) 184 | 185 | # Predicting Probaility 186 | predict_proba = gender_classifier_model.predict_proba(input_array) * 100 187 | 188 | return render_template('gender_classifier.html', 189 | social_data = social_data, 190 | n = number_of_links, 191 | gender_predicted = gender_predicted, 192 | predict_proba = predict_proba) 193 | return render_template('gender_classifier.html', 194 | social_data = social_data, 195 | n = number_of_links) 196 | 197 | 198 | 199 | # Weight Predictor 200 | @ml_app.route('/weight_predictor', methods = ['POST', 'GET']) 201 | def weight_predictor(): 202 | if request.method == 'POST': 203 | # Getting Data from UI 204 | gender = float(request.form['gender']) 205 | height = float(request.form['height']) 206 | 207 | # Forming an Input Array 208 | input_array = [[height, gender]] 209 | 210 | # Loading Weight Predictor Model 211 | weight_predictor_model = pickle.load(open('models/weight_predictor.pkl', 'rb')) 212 | 213 | # Prediction 214 | weight_predicted = round(weight_predictor_model.predict(input_array)[0], 2) 215 | 216 | # Predicting Probaility 217 | # predict_proba = weight_predictor_model.predict_proba(input_array) * 100 218 | 219 | return render_template('weight_predictor.html', 220 | social_data = social_data, 221 | n = number_of_links, 222 | weight_predicted = weight_predicted, 223 | input_array = input_array) 224 | return render_template('weight_predictor.html', 225 | social_data = social_data, 226 | n = number_of_links) 227 | 228 | 229 | # App Launcher 230 | if __name__ == '__main__': 231 | ml_app.run(debug = True, port = 3000) -------------------------------------------------------------------------------- /01.Iris_Species_Classification/README.md: -------------------------------------------------------------------------------- 1 |

Iris Species Classification

2 | 3 | ## Table of Contents 4 | 5 | - [Demo](#demo) 6 | - [Introduction](#introduction) 7 | - [Motivation](#motivation) 8 | - [Project Explanation](#project-explanation) 9 | - [Data Collection](#data-collection) 10 | - [Data Pre processing](#data-pre-processing) 11 | - [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda) 12 | - [Feature Observations](#feature-observations) 13 | - [Feature Selection](#feature-selection) 14 | - [Building Machine Learning Model](#building-machine-learning-model) 15 | - [Model Performance](#model-performance) 16 | - [Build Optimal Model](#build-optimal-model) 17 | - [Predictions](#predictions) 18 | - [Conclusion](#conclusion) 19 | 20 | ## Demo 21 | 22 | **App Link** - [Iris Species Classifier](https://ashok-machine-learning-project.herokuapp.com/#iris) 23 | 24 | ![Iris Gif](../Demos/iris.gif) 25 | 26 | ## Introduction 27 | The Iris flower data set or Fisher’s Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper the use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. 28 | 29 | ## Motivation 30 | This project was started as a motivation for learning Machine Learning Algorithms and to learn the different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model. 31 | 32 | ## Data Collection 33 | - The dataset is collected from [Kaggle](https://www.kaggle.com/uciml/iris). 34 | - This dataset consists of 3 categories of species which is setosa, versicolor and virginica. 35 | - We can find two kind of data from kaggle which is CSV data and SQLITE database. 36 | - Each iris species consists of 50 samples. 37 | - The features of iris flower are Sepal Length in cm, Sepal Width in cm, Petal Length in cm and Petal Width in cm. 38 | ![Image](images/iris_species.png) 39 | 40 | ## Data Pre processing 41 | - We can read the both kind of data by using the below code 42 | 43 | ```python 44 | # TODO : Load Iris Dataset 45 | 46 | # Load Iris csv dataset 47 | iris_csv = pd.read_csv('../data/Iris.csv') 48 | 49 | # Load Iris sqlite data 50 | data = sqlite3.connect('../data/database.sqlite') 51 | query = data.execute('SELECT * FROM Iris') 52 | columns = [col[0] for col in query.description] 53 | iris_db = pd.DataFrame.from_records(data = query.fetchall(), columns = columns) 54 | ``` 55 |
56 | 57 | - The data we collect from kaggle is already pre processed. So there is no need of any pre processing. 58 | 59 | ## Exploratory Data Analysis (EDA) 60 | - Let’s group the data by species and do some descriptive statistics 61 | 62 | ```python 63 | # Groupby Species for descriptive statistics 64 | iris_db.groupby('Species').describe().T 65 | ``` 66 | 67 |
68 | 69 | - **count** shows that there 50 samples for each species. 70 | - **Setosa** 71 | - Average sepal length is 5cm 72 | - Average sepal width is 3cm 73 | - Average petal length is 1.5cm 74 | - Average petal width is 0.25cm 75 | - **Versicolor** 76 | - Average sepal length is 6cm 77 | - Average sepal width is 2.8cm 78 | - Average petal length is 4.26cm 79 | - Average petal width is 1.32cm 80 | - **Virginica** 81 | - Average sepal length is 6.6cm 82 | - Average sepal width is 3cm 83 | - Average petal length is 6cm 84 | - Average petal width is 2cm 85 | 86 | - From the above information, 87 | - Based on Petal length we can easily classify them as *Setosa(1.5cm), Versicolor(4.2cm) and Virginica(6cm)*. 88 | - Based on Petal width we can easily classify *Setosa(0.25cm) from Versicolor(1.32cm) and Virginica(2cm)*. 89 | - Sepal width looks similar for all three species — *Setosa(3cm), Versicolor(2.8cm) and Virginica(3cm)*. 90 | - Based on Sepal length, there are only small changes on three species (5cm, 6cm and 6.6cm) 91 | Since Sepal width looks similar for all the species, we can drop that feature. 92 | 93 | ## Feature Observations 94 | - Let’s plot the correlation between the features. 95 | 96 | ```python 97 | # TODO : Correlation between the features 98 | features_correlation = iris_db.drop('Species', axis = 1).corr() 99 | 100 | # Let's plot an insightful plot on feature correlation 101 | sb.heatmap(data = features_correlation, annot = True, fmt = '.2%') 102 | ``` 103 | 104 |
105 | 106 | #### Let's understand how the Petal length and Petal width contributes together to classify iris species. 107 | ```python 108 | 109 | # Petal Length vs Petal Width 110 | sb.relplot(x = 'PetalLengthCm', y = 'PetalWidthCm', data = iris_db, hue = 'Species', aspect = 2, height = 6) 111 | 112 | ``` 113 |
114 | 115 | - From the above scatter plot, setosa species clearly classified from other two species. 116 | - Other two species can also be classified based on Petal Length and Petal Width easily. 117 | 118 | #### Let's understand how the Petal length and Sepal Length contributes together to classify iris species. 119 | ```python 120 | 121 | # Petal Length vs Sepal Length 122 | sb.relplot(x = 'PetalLengthCm', y = 'SepalLengthCm', data = iris_db, hue = 'Species', aspect = 2, height = 6) 123 | 124 | ``` 125 |
126 | 127 | - From the above plot, we can separate versicolor and virginica by means of Petal Length alone. 128 | - Sepal Length does not contribute more here 129 | 130 | #### Let's understand how the Petal width and Sepal Length contributes together to classify iris species. 131 | ```python 132 | 133 | # Petal Width vs Sepal Length 134 | sb.relplot(x = 'PetalWidthCm', y = 'SepalLengthCm', data = iris_db, hue = 'Species', aspect = 2, height = 6) 135 | 136 | ``` 137 |
138 | 139 | - From the above plot, we can separate based on Petal Width alone. 140 | - Sepal Length doesn't contribute more here. 141 | 142 | ## Feature Selection 143 | - From the above discussion, we can easily classify the iris species based on petal features than sepal features. 144 | 145 | ```python 146 | 147 | # Selecting Iris Features 148 | iris_features = iris_db.loc[:, ['PetalLengthCm', 'PetalWidthCm']] 149 | # Selecting Iris Species 150 | iris_species = iris_db.loc[:, 'Species'] 151 | 152 | ``` 153 | 154 | ## Building Machine Learning Model 155 | - Based on above explorations on the data, we came to know that we can classify the iris species based on some conditions on Petal Length and PetalWidth. 156 | - Let's build a **Decision Tree Classifier** model to the data. 157 | 158 | ```python 159 | 160 | # Converting Pandas DataFrames and Series into Numpy arrays 161 | features_values = iris_features.values 162 | species_values = iris_species.values 163 | 164 | # TODO : Split the dataset as Train set and Test set. 165 | from sklearn.model_selection import train_test_split 166 | X_train, X_test, y_train, y_test = train_test_split(features_values, species_values, 167 | test_size = 0.25, random_state = 42) 168 | 169 | # TODO : Build Decision Tree Classifier Model 170 | from sklearn.tree import DecisionTreeClassifier 171 | decision_tree_classifier = DecisionTreeClassifier(criterion = 'gini') 172 | decision_tree_classifier.fit(X_train, y_train) 173 | 174 | ``` 175 | 176 | ## Model Performance 177 | #### Checking model performance by changing max_depth parameter and the criterion as gini 178 | 179 | ```python 180 | # Initializing Max Depth range 181 | max_depths = list(range(1, 10)) 182 | training_scores = [] 183 | test_scores = [] 184 | for max_depth in max_depths: 185 | decision_tree_classifier = DecisionTreeClassifier(criterion = 'gini', max_depth = max_depth) 186 | decision_tree_classifier.fit(X_train, y_train) 187 | training_scores.append(decision_tree_classifier.score(X_train, y_train)*100) 188 | test_scores.append(decision_tree_classifier.score(X_test, y_test)*100) 189 | 190 | plt.figure(figsize = (14, 6)) 191 | plt.plot(max_depths, training_scores, 'ro-', linewidth = 4, markersize = 15) 192 | plt.plot(max_depths, test_scores, 'go-', linewidth = 4, markersize = 15) 193 | plt.xlabel('MAX DEPTHS', fontsize = 18) 194 | plt.ylabel('SCORES in precent', fontsize = 18) 195 | plt.legend(labels = ['Train scores', 'Test scores'], fontsize=18) 196 | plt.show() 197 | 198 | ``` 199 | 200 |
201 | 202 | ## Build Optimal Model 203 | - Let's build a optimal model 204 | 205 | ```python 206 | 207 | # Optimal Classifier for Iris Dataset 208 | classifier = DecisionTreeClassifier(max_depth = 6, criterion = 'gini') 209 | classifier.fit(X_train, y_train) 210 | 211 | ``` 212 | - Let's plot the Decision Tree 213 | 214 |
215 | 216 | ## Predictions 217 | Let's see the test data predictions 218 | 219 |
220 | 221 | ## Conclusion 222 | 223 | - Finally we finishes the iris classification project. 224 | - We have built a Decision Tree Classifier which performs well with given features petal length and petal width and having the **Training accuracy of 99.11%** and **Testing accuracy of 100%**. 225 | 226 | ### If you love my work, kindly give a Star to my repository. 227 | ### Facing any issues, feel free to post your issues on my GitHub. 228 | ### Thank you Everyone for seeing my work. 229 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

Machine Learning Projects

2 | 3 | ## Table of Contents 4 | - [Demo](#demo) 5 | - [Overview](#overview) 6 | - [Motivation](#motivation) 7 | - [Projects Done](#projects-done) 8 | - [Upcoming Projects](#upcoming-projects) 9 | - [Technical Aspects](#technical-aspects) 10 | - [Installation](#installation) 11 | - [Run](#run) 12 | - [Deployment on Heroku](#deployment-on-heroku) 13 | - [Directory Tree](#directory-tree) 14 | - [To Do](#to-do) 15 | - [Bug / Feature Request](#bug--feature-request) 16 | - [Technologies Used](#technologies-used) 17 | - [Team](#team) 18 | - [License](#license) 19 | - [Credits](#credits) 20 | 21 | ## Demo 22 | - App Link : ashok-machine-learning-projects 23 | 24 | [![All Projects](Demos/main.png)](https://ashok-machine-learning-project.herokuapp.com/) 25 | 26 | ## Overview 27 | This [repository](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects) consists of various machine learning projects in which each projects was done as end to end projects which means from Data Collection through feature engineering, feature selecion to Deployment and Maintainance. The whole app was built with Flask framework. You can launch my app by clicking here. 28 | 29 | For building machine learning models, I have used scikit-learn alias sklearn. 30 | 31 | The model was deployed on Heroku platform which is a platform-as-a-service. 32 | 33 | ## Motivation 34 | While most of the students like me spending their precious time on PUBG, I decided to spend my precious time on learning to gear up my knowledge and my life. During this lockdown period I have learnt countless things related to Data Science and Machine Learning such as Data Preprocesing, Exploratory Data Analysis using NumPy and Pandas, Feature Engineering, Feature Selection, Feature Scaling, Visualization Techniques using Matplotlib and Seaborn and various Machine Learning Algorithms from scratch along with Hyper Parameter Tuning. Later I want to do some projects for the society with my knowledge. So I have started with some beginner level projects to learn some more data related concepts and techniques and step on to some advanced level projects while progressing. 35 | 36 | I always love to share my knowledge with my friends and helps on solving their coding related problems with my best and they also help me on learning new things and keeps motivating me. Though it is a small range of community, I want to share my knowledge to everyone. Whenever I wants relaxation, I write blogs to stay relevant. You can see all my blogs by clicking here. 37 | 38 | ## Projects Done 39 | Here I have listed all the projects that I have done and strats from latest projects. 40 | 41 | | Project No | Project Name | Start Date | End Date | GitHub | Blog | App Link | 42 | |:-----------|:--------------------------------|:------------:|:-----------:|--------------------------:|----------------------:|---------:| 43 | | 5. | Boston Housing Price Prediction | 10-Jul-2020 | 11-Jul-2020 |[Boston Housing](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/05.Boston_House_Price_Prediction)|[Boston Housing](https://medium.com/@Ashok_kumar_K/boston-housing-price-prediction-e279074f356c?source=your_stories_page---------------------------)|-| 44 | | 4. | Loan Status Prediction | 01-Jul-2020 | 07-Jul-2020 |[Loan Status](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/02.Loan_Status_Prediction)|[Loan Status](https://medium.com/analytics-vidhya/loan-status-prediction-machine-learning-model-ca703246c7ec?source=your_stories_page---------------------------)|-| 45 | | 3. | Iris Species Classification | 27-Jun-2020 | 30-Jun-2020 |[Iris Species](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/01.Iris_Species_Classification)|[Iris Species](https://medium.com/analytics-vidhya/iris-species-classification-machine-learning-model-8d7fa4e48f81?source=your_stories_page---------------------------)|-| 46 | | 2. | Gender Classification | 18-Jan-2020 | 31-Jan-2020 |[Gender](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/03.Gender_Classifcation)|-|-| 47 | | 1. | Weight Prediction | 12-Jan-2020 | 17-Jan-2020 |[Weight](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/tree/master/04.Weight_Prediction)|-|-| 48 | 49 | ## Upcoming Projects 50 | | Project No | Project Name | Start Date | End Date | GitHub | Blog | App Link | 51 | |:-----------|:--------------------------------|:------------:|:-----------:|--------------------------:|----------------------:|---------:| 52 | | 6. | Mobile Price Prediction | 15-Jul-2020 | Present |[Mobile Price](#)|[Mobile Price](#)|-| 53 | 54 | 55 | ## Technical Aspects 56 | Since we can deploy atmost 5 app in Heroku as a free service, I came up with a new idea of deploying all my projects in a single web app and controlling the each projects navigation with Flask and Python. 57 | 58 | Some extra-ordinary features that I have included in my app : 59 | - Visualizing Probability of classifcation in each classification type problems. 60 | - Added all the details about the projects such as data source, code source, libraries and frameworks used in each project's description. 61 | - All my end to end projects in a single view. 62 | 63 | ## Installation 64 | To run my app on your local machine, do the following steps. 65 | > **Step 1** : 66 | - I have written the Code with Python 3.7.7. If you don't have Python installed you can find it [here](https://www.python.org/downloads/release/python-377/). 67 | - If you are using a lower version of Python you can upgrade using the pip package, kindly ensure that you have the latest version of pip. 68 | > **Step 2** : 69 | - If you want the current version of my repository to be in your github, you can do forking my repository by clicking as shown in the picture below. 70 | 71 |
72 | 73 | - Clone my repository to your local machine by running the following command. Before doing this, you have to install git on your machine and make sure you are having proper internet connection. 74 | - For **Windows OS** user, open *git bash* and run the following command. 75 | ```bash 76 | git clone https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects.git 77 | ``` 78 | 79 | - For **Linus OS** user, open *Terminal* and run the following command. 80 | ```bash 81 | git clone https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects.git 82 | ``` 83 | 84 | - If you don't want to mess up with all these things, you can just download the *zip* file of my GitHub repository by clicking [here](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/archive/master.zip) and extract it to any file location as your wish and then use it. 85 | - Now we have done with the downloading of my whole project. 86 | 87 | > **Step 3** : 88 | - After downloading the whole repo, get into the main folder by hit the following command in git bash for Windows OS users and Terminal for Linux OS users. 89 | ```bash 90 | cd Machine-Learning-Projects 91 | ``` 92 | 93 | > **Step 4** : 94 | - Now we are going to install all the dependency libraries for this project. Before that you must have Python 3.7.7 and latest version of pip. 95 | - To install all the dependency libraries in a single command, run the following command. 96 | 97 | ```bash 98 | pip install -r requirements.txt 99 | ``` 100 | 101 | > **Step 5** : 102 | - After installing all the dependency libraries, you are ready to run my app on your local machine. 103 | - To launch my app on your local machine, hit the following command. 104 | ```bash 105 | python ml_projects.py 106 | ``` 107 | 108 | ## Run 109 | - Now you have successfully launched my app on your local machine. 110 | - To view my app, hit the following URL in any of the browser such as Chrome, FireFox, etc.., 111 | 112 | ```bash 113 | http://127.0.0.1:3000/ 114 | ``` 115 | 116 | ## Deployment on Heroku 117 | - After successfully running my app on your local machine, you can also deploy my app on heroku by following the steps given on [Heroku Documentation](https://devcenter.heroku.com/articles/getting-started-with-python). You have to do it on your own risk. 118 | - In case, if you have any issues on deploying the app, please open a new issue by clicking [here](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/issues/new/choose). Because it may help others while deploying. 119 | 120 | ## Directory Tree 121 | 122 | 123 | ```bash 124 | Machine-Learning-Projects 125 | ├── 01.Iris_Species_Classification 126 | ├── 02.Loan_Status_Prediction 127 | ├── 03.Gender_Classifcation 128 | ├── 04.Weight_Prediction 129 | ├── 05.Boston_House_Price_Prediction 130 | ├── data 131 | │ ├── ML_Projects.csv 132 | │ └── social-media-links.csv 133 | ├── Demos 134 | ├── models 135 | ├── py_modules 136 | │ ├── __pycache__ 137 | │ └── data_extraction.py 138 | ├── static 139 | │ ├── css 140 | │ ├── files 141 | │ ├── images 142 | │ └── js 143 | ├── templates 144 | │ ├── base.html 145 | │ ├── all_projects.html 146 | │ ├── weight_predictor.html 147 | │ ├── gender_classifier.html 148 | │ ├── iris_species_classifier.html 149 | │ ├── loan_status_predictor.html 150 | │ └── boston_house_price_predictor.html 151 | ├── LICENSE.txt 152 | ├── ml_projects.py 153 | ├── Procfile 154 | ├── requirements.txt 155 | ├── runtime.txt 156 | └── README.md 157 | ``` 158 | 159 | 160 | ## To Do 161 | 162 | These features will be added in the future : 163 | 1. Connecting my portfolio app with this project app very soon. 164 | 2. Using Exception handling for handling invalid input. 165 | 3. Adding a link to the data source in the project description popup modal. 166 | 4. Adding GitHub link to each projects in both description popup modal and project page. 167 | 5. Adding better visualization charts for classification type problems. 168 | 6. Adding visualization for regrerssion type problems. 169 | 170 | ## Bug / Feature Request 171 | 172 | If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue [here](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/issues/new) by including your search query and the expected result. 173 | 174 | If you'd like to request a new function, feel free to do so by opening an issue [here](https://github.com/ASHOKKUMAR-K/Machine-Learning-Projects/issues/new/choose). Please include sample queries and their corresponding results. 175 | 176 | ## Technologies Used 177 | 178 | PythonAnacondaFlaskMatplotlib 179 | Seabornscikit-learnPandasNumPygunicornbootstrap 180 | 181 | 182 | ## Team 183 | [](https://ashok-machine-learning-project.herokuapp.com/) | 184 | -| 185 | [ASHOK KUMAR K](https://ashok-machine-learning-project.herokuapp.com) |) 186 | 187 | ## License 188 | Copyright 2020 ASHOK KUMAR K 189 | 190 | Licensed under the Apache License, Version 2.0 (the "License"); 191 | you may not use this file except in compliance with the License. 192 | You may obtain a copy of the License at 193 | 194 | http://www.apache.org/licenses/LICENSE-2.0 195 | 196 | Unless required by applicable law or agreed to in writing, software 197 | distributed under the License is distributed on an "AS IS" BASIS, 198 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 199 | See the License for the specific language governing permissions and 200 | limitations under the License. 201 | 202 | ## Credits 203 | 1. Kaggle - I have collected dataset from kaggle for some of the projects such as Loan Status Prediction, Iris Species Classification, Boston House Price Prediction, etc.., 204 | -------------------------------------------------------------------------------- /05.Boston_House_Price_Prediction/README.md: -------------------------------------------------------------------------------- 1 |

BOSTON HOUSING PRICE PREDICTION

2 | 3 | # Table of Contents 4 | - [Demo](#demo) 5 | - [Introduction](#introduction) 6 | - [About the Dataset](#about-the-dataset) 7 | - [Dataset Naming](#dataset-naming) 8 | - [Miscellaneous Details](#miscellaneous-details) 9 | - [Origin](#origin) 10 | - [Usage](#usage) 11 | - [Number of Cases](#number-of-cases) 12 | - [Variables](#variables) 13 | - [Motivation](#motivation) 14 | - [Project Explanation](#project-explanation) 15 | - [Data Collection](#data-collection) 16 | - [Feature Engineering](#feature-engineering) 17 | - [Feature Observations](#feature-observations) 18 | - [Feature Selection](#feature-selection) 19 | - [Building Machine Learning Models](#building-machine-learning-models) 20 | - [Model Performances](#model-performances) 21 | - [Building Optimum Model](#building-optimum-model) 22 | - [Conclusion](#conclusion) 23 | 24 | ## Demo 25 | **App Link** - [Boston House Price Predictor](https://ashok-machine-learning-project.herokuapp.com/#boston) 26 | 27 | ![Boston Price Gif](../Demos/boston.gif) 28 | 29 | ## Introduction 30 | ### About the Dataset 31 | This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive [Data](http://lib.stat.cmu.edu/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases. 32 | 33 | The data was originally published by Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978`. 34 | 35 | ### Dataset Naming 36 | The name for this dataset is simply boston. It has two prototasks: nox, in which the nitrous oxide level is to be predicted; and price, in which the median value of a home is to be predicted. 37 | 38 | ### Miscellaneous Details 39 | #### Origin 40 | - The origin of the boston housing data is Natural. 41 | #### Usage 42 | - This dataset may be used for Assessment. 43 | #### Number of Cases 44 | - The dataset contains a total of 506 cases. 45 | #### Order 46 | - The order of the cases is mysterious. 47 | #### Variables 48 | There are 14 attributes in each case of the dataset. They are: 49 | ```bash 50 | 1. CRIM - per capita crime rate by town 51 | 2. ZN - proportion of residential land zoned for lots over 25,000 sq.ft. 52 | 3. INDUS - proportion of non-retail business acres per town. 53 | 4. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise) 54 | 5. NOX - nitric oxides concentration (parts per 10 million) 55 | 6. RM - average number of rooms per dwelling 56 | 7. AGE - proportion of owner-occupied units built prior to 1940 57 | 8. DIS - weighted distances to five Boston employment centres 58 | 9. RAD - index of accessibility to radial highways 59 | 10. TAX - full-value property-tax rate per 10,000 dollars. 60 | 11. PTRATIO - pupil-teacher ratio by town 61 | 12. B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 62 | 13. LSTAT - % lower status of the population 63 | 14. MEDV - Median value of owner-occupied homes in 1000's dollars 64 | ``` 65 | - **Note** 66 | - Variable #14 seems to be censored at 50.00 (corresponding to a median price of 50,000 dollars); Censoring is suggested by the fact that the highest median price of exactly 50,000 dollars is reported in 16 cases, while 15 cases have prices between 40,000 dollars and 50,000 dollars, with prices rounded to the nearest hundred. Harrison and Rubinfeld do not mention any censoring. 67 | 68 | ## Motivation 69 | - This project was started as a motivation for learning Machine Learning Algorithms and to learn the different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model. 70 | 71 | - In this project we will predicts house price in boston city. 72 | 73 | ## Project Explanation 74 | ### Data Collection 75 | - It was obtained from the StatLib archive [Data](http://lib.stat.cmu.edu/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases. 76 | - The dataset is collected from [Kaggle](https://www.kaggle.com/vikrishnan/boston-house-prices). 77 | 78 | ##### Loading the collected data 79 | - The CSV data is loaded with the help of [read_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) method in pandas library. 80 | - The dataset consists of 506 samples and 14 features with 1 prediction feature. 81 | 82 | ```python 83 | # Initializing column names 84 | columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] 85 | 86 | # Loading Boston Housing Dataset 87 | boston = pd.read_csv('../data/housing.csv', delimiter=r"\s+", names = columns) 88 | ``` 89 | 90 | Let's see the number of samples and factors 91 | ```python 92 | # TODO : Let's know how many factors of an individual and Number of Samples 93 | print("The Boston housing Price Prediction Dataset has") 94 | print("\t\tNumber of Factors : \t", boston.shape[1] - 1) 95 | print("\t\tNumber of Samples : \t", boston.shape[0]) 96 | 97 | OUTPUT : 98 | The Boston housing Price Prediction Dataset has 99 | Number of Factors : 13 100 | Number of Samples : 506 101 | ``` 102 | ### Feature Engineering 103 | Let's check for null values. 104 | ```python 105 | # TODO : Check for null values and visualizing it using heatmap 106 | boston.isnull().sum() 107 | OUTPUT : 108 | CRIM 0 109 | ZN 0 110 | INDUS 0 111 | CHAS 0 112 | NOX 0 113 | RM 0 114 | AGE 0 115 | DIS 0 116 | RAD 0 117 | TAX 0 118 | PTRATIO 0 119 | B 0 120 | LSTAT 0 121 | MEDV 0 122 | dtype: int64 123 | ``` 124 | ###### Wowwww!!!! There are no null values in any of the features. 125 | 126 | Let's check for Datatypes of each features. 127 | ```python 128 | # TODO : Let's check for data types of all the columns 129 | boston.dtypes 130 | 131 | OUTPUT : 132 | 133 | CRIM float64 134 | ZN float64 135 | INDUS float64 136 | CHAS int64 137 | NOX float64 138 | RM float64 139 | AGE float64 140 | DIS float64 141 | RAD int64 142 | TAX float64 143 | PTRATIO float64 144 | B float64 145 | LSTAT float64 146 | MEDV float64 147 | dtype: object 148 | ``` 149 | Though all the features are numerical values, we are going to see the distribution of data and some are categorical also. 150 | 151 |
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
152 | 153 | ### Feature Observations 154 | Let's see the relation between each and every features 155 | 156 | ![pairplot](images/pairplot.png) 157 | 158 | Let's see the correlation between the features. 159 | 160 | ![Features Correlation](images/features_correlation.png) 161 | 162 | Let's see the correlation with prediction column 163 | 164 | ![Correlation with MEDV](images/correlation_with_price.png) 165 | 166 | ### Feature Selection 167 | ```python 168 | # Let's see the features having more correlation 169 | corr_with_medv[:-1].abs().sort_values(ascending = False) 170 | ``` 171 | 172 | ```bash 173 | LSTAT 0.737663 174 | RM 0.695360 175 | PTRATIO 0.507787 176 | INDUS 0.483725 177 | TAX 0.468536 178 | NOX 0.427321 179 | CRIM 0.388305 180 | RAD 0.381626 181 | AGE 0.376955 182 | ZN 0.360445 183 | B 0.333461 184 | DIS 0.249929 185 | CHAS 0.175260 186 | dtype: float64 187 | ``` 188 | ###### Let's confirm this by using ExtraTreesRegressor 189 | 190 | ```python 191 | # TODO : To know the feature Importances 192 | y = boston['MEDV'].values 193 | from sklearn.ensemble import ExtraTreesRegressor 194 | etc = ExtraTreesRegressor() 195 | etc.fit(boston.iloc[:, :-1].values, y) 196 | 197 | print("Percentage Importance of each features with respect to House Price : ") 198 | important_features = pd.Series(etc.feature_importances_*100, index = boston.columns[:-1]) 199 | 200 | # Feature Impotances by ExtraTressRegressor 201 | important_features.sort_values(ascending = False) 202 | ``` 203 | 204 | ```bash 205 | OUTPUT : 206 | LSTAT 35.155692 207 | RM 33.497965 208 | PTRATIO 5.909181 209 | NOX 3.974996 210 | INDUS 3.911079 211 | TAX 3.886847 212 | DIS 2.984446 213 | CRIM 2.923965 214 | RAD 1.990202 215 | B 1.938884 216 | AGE 1.820047 217 | CHAS 1.599084 218 | ZN 0.407612 219 | dtype: float64 220 | ``` 221 | 222 | Approximately both correlation matrix and ExtraTreesRegressor says the same result. 223 | 224 | Lets's plot the feaure importances with Matplotlib. 225 | ![Feature Importance](images/feature_importances.png) 226 | 227 | From the above feature observations and importances, we found that some columns are most important such as *LSTAT*, *RM*, *PTRATIO*, *NOX*, *INDUS* and *TAX*. 228 | 229 | Let's arrange the features based on feature importance. 230 | ```python 231 | # Arranging columns based on features importance 232 | new_boston = boston[features_arranged_on_importance] 233 | ``` 234 | 235 | ### Building Machine Learning Models 236 | 237 | ``` 238 | # Getting boston values 239 | X = new_boston.values 240 | X = X[:, :6] 241 | 242 | # TODO : Splitting data as train and test data 243 | from sklearn.model_selection import train_test_split 244 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42) 245 | ``` 246 | ##### Linear Regression 247 | ```python 248 | from sklearn.linear_model import LinearRegression 249 | 250 | linear_model = LinearRegression() 251 | linear_model.fit(X_train, y_train) 252 | 253 | print('Training Score : ', linear_model.score(X_train, y_train)) 254 | print('Testing Score : ', linear_model.score(X_test, y_test)) 255 | 256 | print('R2 Score : ', r2_score(y_test, linear_model.predict(X_test))) 257 | print('MSE : ', mean_squared_error(y_test, linear_model.predict(X_test))) 258 | ``` 259 | 260 | ```bash 261 | Training Score : 0.6906984575653774 262 | Testing Score : 0.6209548378098407 263 | R2 Score : 0.6209548378098407 264 | MSE : 27.796815732022583 265 | ``` 266 | 267 | ##### Decision Tree Regression 268 | Let's see how the different decision tree train for the given data. 269 | 270 | ![DTR](images/random_decision_tree_regressor.png) 271 | 272 | See how the decision tree score changes for different random states 273 | 274 | ```python 275 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42) 276 | 277 | dtr_model = DecisionTreeRegressor(max_depth=23, random_state=3) 278 | dtr_model.fit(X_train[:, :], y_train) 279 | 280 | print('Training Score : ', dtr_model.score(X_train, y_train)) 281 | print('Testing Score : ', dtr_model.score(X_test, y_test)) 282 | print('R2 Score : ', r2_score(y_test, dtr_model.predict(X_test))) 283 | print('MSE : ', mean_squared_error(y_test, dtr_model.predict(X_test))) 284 | ``` 285 | 286 | ```bash 287 | Training Score : 1.0 288 | Testing Score : 0.8815969829691199 289 | R2 Score : 0.8815969829691199 290 | MSE : 8.682941176470589 291 | ``` 292 | 293 | ##### Random Forest Regression 294 | ```python 295 | from sklearn.ensemble import RandomForestRegressor 296 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42) 297 | rfr = RandomForestRegressor(max_depth = 7, random_state = 63) 298 | rfr.fit(X_train, y_train) 299 | 300 | print('Training Score : ', rfr.score(X_train, y_train)) 301 | print('Testing Score : ', rfr.score(X_test, y_test)) 302 | print('R2 Score : ', r2_score(y_test, rfr.predict(X_test))) 303 | print('MSE : ', mean_squared_error(y_test, rfr.predict(X_test))) 304 | ``` 305 | 306 | ```bash 307 | Training Score : 0.9560507818314905 308 | Testing Score : 0.8584494627144632 309 | R2 Score : 0.8584494627144632 310 | MSE : 10.380436407524767 311 | ``` 312 | 313 | ##### Model accuracy of different models 314 | ```bash 315 | Linear Regression : 316 | Score : 0.6209548378098407 317 | 318 | Decision Tree Regression : 319 | Score : 0.8815969829691199 320 | 321 | Random Forest Regression : 322 | Score : 0.8584494627144632 323 | 324 | k Neighbors Regression : 325 | Score : 0.6094868472810563 326 | ``` 327 | 328 | ### Model Performances 329 | ```python 330 | X_train, X_test, y_train, y_test = train_test_split(X[:, :], y, test_size = 0.20, random_state = 46) 331 | 332 | print('Random Forest Regression : ') 333 | random_forest_regressor = RandomForestRegressor(max_depth = 7, random_state = 63) 334 | random_forest_regressor.fit(X_train, y_train) 335 | print('Score : ', random_forest_regressor.score(X, y)) 336 | ``` 337 | 338 | ```bash 339 | OUTPUT : 340 | Random Forest Regression : 341 | Score : 0.944479365914758 342 | ``` 343 | 344 | Let's see the score of Random forest model for different training samples. 345 | 346 | ![Random forest for different training samples](images/random_forest_diff_train_samples.png) 347 | 348 | Let's see the score of different Random forest model. 349 | 350 | ![Different Random forest Model](images/random_forest_diff_RF_models.png) 351 | 352 | Let's see the score of Random forest model for different max_depths. 353 | 354 | ![Random forest for different Max Depths](images/random_forest_diff_max_depth.png) 355 | 356 | From this, we are going to choose, 357 | - random_state = 3, for choosing random Training samples 358 | - random_state = 68, for random Random forest regressor 359 | - max_depth = 13, for Max Depths in random forest regressor 360 | 361 | ### Building Optimum Model 362 | 363 | ```python 364 | # Choosing Optimal Training Samples 365 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 3) 366 | 367 | # Building Optimal Random Forest regressor Model 368 | random_forest_regressor = RandomForestRegressor(max_depth = 13, random_state = 68) 369 | random_forest_regressor.fit(X_train, y_train) 370 | ``` 371 | 372 | ```python 373 | RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse', 374 | max_depth=13, max_features='auto', max_leaf_nodes=None, 375 | max_samples=None, min_impurity_decrease=0.0, 376 | min_impurity_split=None, min_samples_leaf=1, 377 | min_samples_split=2, min_weight_fraction_leaf=0.0, 378 | n_estimators=100, n_jobs=None, oob_score=False, 379 | random_state=68, verbose=0, warm_start=False) 380 | ``` 381 | 382 | ```python 383 | print('Training Accuracy : ', random_forest_regressor.score(X_train, y_train)) 384 | print('Testing Accuracy : ', random_forest_regressor.score(X_test, y_test)) 385 | print('Mean Squared Error : ', mean_squared_error(y_test, random_forest_regressor.predict(X_test))) 386 | print('Root Mean Squared Error : ', mean_squared_error(y_test, random_forest_regressor.predict(X_test))**0.5) 387 | print('Score : ', r2_score(y, random_forest_regressor.predict(X))) 388 | 389 | OUTPUT : 390 | 391 | Training Accuracy : 0.9789853943215869 392 | Testing Accuracy : 0.9202169918339218 393 | Mean Squared Error : 6.602427120371769 394 | Root Mean Squared Error : 2.569518849974012 395 | Score : 0.9673793783596027 396 | ``` 397 | 398 | ## Conclusion 399 | ##### Finally we finishes the project. We have built a Random Forest Regressor Model which performs well with top 6 features and having the Training accuracy of 97.89% and Testing accuracy of 96.73%. 400 | 401 | ##### Thank you Everyone for reading my notebook. 402 | 403 | ##### If you love my work, kindly give a Star to my repository. 404 | ##### Facing any issues, feel free to post your issues on my GitHub. 405 | --------------------------------------------------------------------------------