├── IMPORTANT NOTE.docx ├── Installing-Python_Teclov.pdf ├── LICENSE ├── README.md ├── Section 1 └── code-LR-Teclov │ ├── Case_study Simple+Linear+Regression.ipynb │ └── tvmarketing.csv ├── Section 10 └── SVM-2 │ ├── Spam.csv │ ├── Spam.txt │ ├── Teclov_LinearSVM.ipynb │ ├── Teclov_Non-Linear+SVM.ipynb │ ├── Teclov_SVM_casestudy3.ipynb │ ├── Teclov_kaggle_casestudy4.ipynb │ ├── letter-recognition.csv │ └── train and test csv files.zip ├── Section 11 └── Decision Tree │ ├── DecisionTree_case study Teclov.ipynb │ └── adult_dataset.csv ├── Section 12 ├── Boosting │ ├── TeclovBoosting+-+TalkingData+Click+Fraud+.ipynb │ ├── Teclov_Adaboost_cancer_prediction.ipynb │ └── train_sample.csv └── Random_Forest │ ├── Teclov Random+Forest+-+Credit+Default+Prediction.ipynb │ └── credit-card-default.csv ├── Section 14 └── Unsupervised │ ├── Online+Retail.zip │ └── TeclovK--Mean--Clustering.ipynb ├── Section 15 └── PCA │ ├── Teclov_PCA_casestudy1.ipynb │ └── train file.zip ├── Section 16 └── AdvanceReg │ ├── CarPrice_Assignment.csv │ ├── Car_price_data_Dictonary.xlsx │ ├── TeclovCar+Price+Prediction+-+Ridge+and+Lasso+Regression.ipynb │ ├── Teclov_generalised_regression.ipynb │ ├── total-electricity-consumption-us (1).csv │ └── total-electricity-consumption-us.csv ├── Section 17 └── Teclov-Project-Medical-treatment.ipynb │ └── Teclov Project - Medical treatment.ipynb ├── Section 19 └── Quora Project │ ├── 2Teclov Document Similarity1.ipynb │ ├── 4.ML_models.ipynb │ ├── Quora Project.zip │ ├── Teclov1.Quora.ipynb │ └── Teclov3_W2V.ipynb ├── Section 2 └── Multiple Linear Regression │ ├── Housing.csv │ ├── Housing_Case_Study teclov.ipynb │ ├── Housing_RFE.ipynb │ ├── Multiple+Linear+Regression.ipynb │ └── advertising.csv ├── Section 20 └── Investment Requirement Analysis │ └── Teclov_Investment Requirement Analysis │ ├── Teclov Project description.pdf │ ├── Teclov_1_Data_Cleaning.ipynb │ ├── companies.txt │ ├── mapping.csv │ ├── rounds2.csv │ ├── teclov_2_Data_Cleaning.ipynb │ └── teclov_3_Analysis.ipynb ├── Section 21 └── Teclov_Loan Analysis │ ├── Data_Dictionary.xlsx │ ├── Problem_statement.pdf │ ├── Teclov_Loan.ipynb │ ├── Thumbs.db │ └── loan.zip ├── Section 22 └── Car Project │ ├── CarPrice_Assignment.csv │ ├── Data Dictionary - carprices.xlsx │ ├── TeclovCar+Price+Prediction.ipynb │ └── Teclov_Problemdesc.pdf ├── Section 23 └── TeclovFacebook.ipynb │ └── TeclovFacebook.ipynb ├── Section 3 └── Hotstar_Netflix_Case Study │ ├── Media+Company.ipynb │ └── mediacompany.csv ├── Section 4 └── Gradient-Descent-Updated │ ├── Gradient_Descent_Updatd.ipynb │ ├── gd.PNG │ ├── gd1.PNG │ └── gd2.PNG ├── Section 5 └── KNN │ ├── demo_data │ ├── 1.ushape.csv │ ├── 2.concerticcir1.csv │ ├── 3.concertriccir2.csv │ ├── 4.linearsep.csv │ ├── 5.outlier.csv │ ├── 6.overlap.csv │ ├── 7.xor.csv │ ├── 8.twospirals.csv │ ├── 9.random.csv │ └── meshgrid_image.png │ └── teclov_knn.ipynb ├── Section 7 ├── Case 1 │ ├── CarPrice_Assignment.csv │ ├── Housing.csv │ ├── 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