├── CREDIT CARD FRAUD DETECTION └── credit card fraud detections │ └── Anomly Detection for CC Fraud.ipynb ├── IRIS FLOWER CLASSIFICATION ├── IRIS DATASET │ └── Iris.csv └── iris classification │ └── iris classification.ipynb ├── MOVIE RATING PREDICTION WITH PYTHON ├── Jupyter notebook Checkpoint │ └── Movie-checkpoint.ipynb ├── Movie dataset.csv └── Movie.ipynb ├── README.md ├── SALES PREDICTION USING PYTHON └── Sales prediction │ └── Sales Prediction.ipynb └── TITANIC SURVIVAL PREDICTION ├── LICENSE.txt ├── README.md ├── Titanic CSV ├── titanic.csv ├── titanic_test.csv └── train.csv └── Titanic Data Science Project └── titanic project.ipynb /IRIS FLOWER CLASSIFICATION/IRIS DATASET/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 | -------------------------------------------------------------------------------- /MOVIE RATING PREDICTION WITH PYTHON/Movie dataset.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rubydamodar/codsoft-ds-projects/949616f33f3095cc754d73c66e9ab17d8bae1738/MOVIE RATING PREDICTION WITH PYTHON/Movie dataset.csv -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | > ### main owner contributor - Rohit Raj 2 | ![image](https://github.com/user-attachments/assets/ef90a255-2ff1-4455-8de7-e346bcfeaae5) 3 | ![image](https://github.com/user-attachments/assets/fc89ad24-7f08-468d-ae95-46c3d6775c63) 4 | # 🌟 CODSOFT Data Science Internship🌟 5 | 6 | Welcome to my GitHub repository for the **CODSOFT Data Science Internship**! I’m thrilled to share my journey as a BCA (Data Science) student at Amity University, showcasing the projects I completed during this enriching experience. This repository highlights my hands-on work, skills development, and the growth I achieved as an aspiring data scientist. 7 | 8 | ## ✨ About CODSOFT 9 | CODSOFT is a dynamic and inspiring community that brings together passionate individuals aiming to develop their skills and foster leadership. Through engaging mentorship programs, workshops, and collaborative projects, CODSOFT empowers learners to excel and make impactful contributions in their chosen fields. 🌱 10 | 11 | ## 📂 Projects and Tasks 12 | 13 | ### 1. 🚢 Titanic Survival Prediction 14 | **Objective**: Predict whether a passenger on the Titanic survived or not. 15 | **Dataset**: Includes data on age, gender, ticket class, fare, and more. 16 | **What I Learned**: 17 | - 📊 **Data Cleaning & Preprocessing**: Handling missing data and preparing it for analysis. 18 | - 🔍 **Exploratory Data Analysis (EDA)**: Gaining insights through visualization. 19 | - ⚙️ **Modeling**: Building and fine-tuning classification algorithms. 20 | - 📈 **Evaluation**: Assessing model performance with accuracy and other metrics. 21 | 22 | ### 2. 🎬 Movie Rating Prediction 23 | **Objective**: Predict movie ratings based on features like genre, director, and actors. 24 | **What I Learned**: 25 | - 🛠 **Feature Engineering**: Selecting and creating relevant features. 26 | - 📈 **Regression Techniques**: Implementing and tuning regression models. 27 | - 📊 **Data Visualization**: Using visual tools to understand relationships and patterns. 28 | - ✅ **Model Validation**: Ensuring reliable and accurate predictions. 29 | 30 | ### 3. 🌼 Iris Flower Classification 31 | **Objective**: Classify the Iris species (setosa, versicolor, virginica) based on petal and sepal measurements. 32 | **What I Learned**: 33 | - 📊 **Data Visualization**: Visualizing distributions and feature relationships. 34 | - 🤖 **Model Training**: Applying and comparing classification algorithms. 35 | - 🧪 **Evaluation Metrics**: Using accuracy and confusion matrices to measure model success. 36 | 37 | ### 4. ✍️ Handwritten Digit Recognition 38 | **Objective**: Recognize handwritten digits from the MNIST dataset. 39 | **What I Learned**: 40 | - 🖥️ **Deep Learning**: Building neural networks with frameworks like TensorFlow/Keras. 41 | - 🔧 **Data Augmentation**: Enhancing training data for better model performance. 42 | - 📊 **Model Assessment**: Evaluating precision, recall, and overall accuracy. 43 | 44 | ### 5. 📈 Stock Price Forecasting 45 | **Objective**: Forecast stock prices using historical data. 46 | **What I Learned**: 47 | - ⏳ **Time Series Analysis**: Understanding trends, seasonality, and patterns in data. 48 | - 🔄 **Predictive Models**: Implementing ARIMA and LSTM models for forecasting. 49 | - 📉 **Model Evaluation**: Measuring performance with RMSE and other error metrics. 50 | 51 | ## 🏆 Skills I Developed 52 | - **Data Cleaning & Preprocessing** 🧼 53 | - **Feature Engineering** 🛠 54 | - **Exploratory Data Analysis** 📊 55 | - **Machine Learning & Deep Learning** 🤖 56 | - **Model Validation & Evaluation** 📈 57 | 58 | ## 🌟 Conclusion 59 | Completing these projects has significantly strengthened my understanding of data science principles, machine learning algorithms, and practical problem-solving skills. I am proud to have applied these learnings in a real-world context and grown as a data science professional. 60 | 61 | Thank you for exploring my work! 62 | **Ruby Poddar** 63 | *BCA (Data Science), Amity University* 🌟 64 | -------------------------------------------------------------------------------- /TITANIC SURVIVAL PREDICTION/LICENSE.txt: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Ruby Poddar 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /TITANIC SURVIVAL PREDICTION/README.md: -------------------------------------------------------------------------------- 1 | 2 | # Titanic Survival Prediction 3 | ![Titanic](https://i.pinimg.com/564x/70/3e/02/703e028f8acef43ae65e79c4491cff60.jpg) 4 | 5 | ## Titanic Survival Prediction 6 | 7 | This Repositorie aims to predict the survival of passengers aboard the Titanic using machine learning techniques. The dataset includes various attributes such as age, gender, ticket class, and more, which are analyzed to build a predictive model. The project involves data loading, preprocessing, feature engineering, model selection, training, evaluation, and result interpretation. The final model predicts survival probabilities based on passenger features. 8 | 9 | --- 10 | ![Titanic](https://i.pinimg.com/564x/98/73/c6/9873c68f08671ca72aece2d1ceb6b93b.jpg) 11 | --- 12 | 13 | ### Repository Structure 14 | 15 | ``` 16 | ├── Titanic_CSV/ 17 | │ ├── titanic.csv # Complete Titanic dataset 18 | │ ├── titanic_train.csv # Training dataset 19 | │ ├── titanic_test.csv # Testing dataset 20 | ├── Titanic_Data_Science_Project/ 21 | │ ├── titanic_project.ipynb # Jupyter notebook containing the project code 22 | ├── LICENSE.txt # License information for the project 23 | └──README.md # Overview and description of the project 24 | ``` 25 | 26 | #### Explanation 27 | 28 | - **Titanic_CSV/**: This directory contains all the datasets used in the project. 29 | - **titanic.csv**: The complete Titanic dataset with all the available features. 30 | - **titanic_train.csv**: The training dataset, which includes features and the target variable (survival) used to train the machine learning models. 31 | - **titanic_test.csv**: The testing dataset, which includes features but not the target variable, used to evaluate the performance of the trained models. 32 | 33 | - **Titanic_Data_Science_Project/**: This directory contains the Jupyter notebook with the project code and analysis. 34 | - **titanic_project.ipynb**: The main Jupyter notebook containing all the code for data loading, preprocessing, feature engineering, model training, evaluation, and result interpretation. 35 | 36 | - **LICENSE**: The file that contains the license information for the project, specifying the terms under which the project can be used and distributed. 37 | 38 | - **README.md**: The readme file that provides an overview and description of the project, instructions for running the code, and any other relevant information. 39 | 40 | --- 41 | # Introduction 42 | Hey there! Welcome to my Titanic Survival Prediction project. I'm **Ruby Poddar**, and I invite you to explore this classic dataset with me. The sinking of the Titanic in 1912 remains one of the deadliest maritime disasters in history. This project dives deep into the Titanic dataset, aiming to unravel patterns and insights that determined passenger survival. By leveraging machine learning techniques, we'll predict survival outcomes based on passenger attributes such as age, gender, ticket class, and more. 43 | 44 | 45 | --- 46 | 47 | ### Tools: 48 | - Jupyter Notebook 49 | 50 | ### Libraries: 51 | - **Numpy**: For numerical computing with arrays and matrices. 52 | - **pandas**: For data manipulation and analysis. 53 | - **Matplotlib**: For creating static, animated, and interactive visualizations in Python. 54 | - **scikit-learn**: For machine learning algorithms, preprocessing, model selection, and evaluation. 55 | - **seaborn**: For statistical data visualization based on Matplotlib, providing a high-level interface for drawing attractive and informative statistical graphics. 56 | 57 | 58 | --- 59 | 60 | # Data Exploration 61 | 62 | The command `titanic_df.head(10)` in Python with pandas displays the first 10 rows of the DataFrame `titanic_df`. It's used to quickly preview the dataset's initial rows for inspection and analysis. 63 | 64 | ### `titanic_df.head(10)` 65 | 66 | --- 67 | | | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | 68 | |---|-------------|----------|--------|------|-----|-----|-------|-------|--------|------|-------|----------| 69 | | 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | 70 | | 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | 71 | | 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 72 | | 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | 73 | | 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S | 74 | | 5 | 6 | 0 | 3 | Moran, Mr. James | male | NaN | 0 | 0 | 330877 | 8.4583 | NaN | Q | 75 | | 6 | 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54.0 | 0 | 0 | 17463 | 51.8625 | E46 | S | 76 | | 7 | 8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2.0 | 3 | 1 | 349909 | 21.0750 | NaN | S | 77 | | 8 | 9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27.0 | 0 | 2 | 347742 | 11.1333 | NaN | S | 78 | | 9 | 10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14.0 | 1 | 0 | 237736 | 30.0708 | NaN | C | 79 | 80 | ---- 81 | 82 | ### `train_df` 83 | 84 | 85 | --- 86 | 87 | | PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | 88 | |--------------:|---------:|:----------------------------------------------|:-------|------:|--------:|--------:|:---------|--------:|:--------|:-----------| 89 | | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q | 90 | | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47 | 1 | 0 | 363272 | 7 | NaN | S | 91 | | 894 | 2 | Myles, Mr. Thomas Francis | male | 62 | 0 | 0 | 240276 | 9.6875 | NaN | Q | 92 | | 895 | 3 | Wirz, Mr. Albert | male | 27 | 0 | 0 | 315154 | 8.6625 | NaN | S | 93 | | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22 | 1 | 1 | 3101298 | 12.2875 | NaN | S | 94 | | 897 | 3 | Svensson, Mr. Johan Cervin | male | 14 | 0 | 0 | 7538 | 9.225 | NaN | S | 95 | | 898 | 3 | Connolly, Miss. Kate | female | 30 | 0 | 0 | 330972 | 7.6292 | NaN | Q | 96 | | 899 | 2 | Caldwell, Mr. Albert Francis | male | 26 | 1 | 1 | 248738 | 29 | NaN | S | 97 | | 900 | 3 | Abrahim, Mrs. Joseph (Sophie Halaut Easu) | female | 18 | 0 | 0 | 2657 | 7.2292 | NaN | C | 98 | | 901 | 3 | Davies, Mr. John Samuel | male | 21 | 2 | 0 | A/4 48871| 24.15 | NaN | S | 99 | 100 | 101 | --- 102 | 103 | ### `titanic_df.describe()` 104 | 105 | --- 106 | 107 | | | PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | 108 | |----------|-------------|----------|--------|----------|----------|----------|-----------| 109 | | count | 891.000000 | 891.000000 | 891.000000 | 714.000000 | 891.000000 | 891.000000 | 891.000000 | 110 | | mean | 446.000000 | 0.383838 | 2.308642 | 29.699118 | 0.523008 | 0.381594 | 32.204208 | 111 | | std | 257.353842 | 0.486592 | 0.836071 | 14.526497 | 1.102743 | 0.806057 | 49.693429 | 112 | | min | 1.000000 | 0.000000 | 1.000000 | 0.420000 | 0.000000 | 0.000000 | 0.000000 | 113 | | 25% | 223.500000 | 0.000000 | 2.000000 | 20.125000 | 0.000000 | 0.000000 | 7.910400 | 114 | | 50% | 446.000000 | 0.000000 | 3.000000 | 28.000000 | 0.000000 | 0.000000 | 14.454200 | 115 | | 75% | 668.500000 | 1.000000 | 3.000000 | 38.000000 | 1.000000 | 0.000000 | 31.000000 | 116 | | max | 891.000000 | 1.000000 | 3.000000 | 80.000000 | 8.000000 | 6.000000 | 512.329200 | 117 | 118 | 119 | --- 120 | 121 | ### `train_df.describe()` 122 | 123 | --- 124 | 125 | | | PassengerId | Pclass | Age | SibSp | Parch | Fare | 126 | |----------|-------------|-----------|-----------|-----------|-----------|------------| 127 | | count | 418.000000 | 418.000000 | 332.000000 | 418.000000 | 418.000000 | 417.000000 | 128 | | mean | 1100.500000 | 2.265550 | 30.272590 | 0.447368 | 0.392344 | 35.627188 | 129 | | std | 120.810458 | 0.841838 | 14.181209 | 0.896760 | 0.981429 | 55.907576 | 130 | | min | 892.000000 | 1.000000 | 0.170000 | 0.000000 | 0.000000 | 0.000000 | 131 | | 25% | 996.250000 | 1.000000 | 21.000000 | 0.000000 | 0.000000 | 7.895800 | 132 | | 50% | 1100.500000 | 3.000000 | 27.000000 | 0.000000 | 0.000000 | 14.454200 | 133 | | 75% | 1204.750000 | 3.000000 | 39.000000 | 1.000000 | 0.000000 | 31.500000 | 134 | | max | 1309.000000 | 3.000000 | 76.000000 | 8.000000 | 9.000000 | 512.329200 | 135 | 136 | 137 | 138 | --- 139 | 140 | ### `titanic_df.info()` 141 | ```markdown 142 | 143 | RangeIndex: 891 entries, 0 to 890 144 | Data columns (total 12 columns): 145 | # Column Non-Null Count Dtype 146 | --- ------ -------------- ----- 147 | 0 PassengerId 891 non-null int64 148 | 1 Survived 891 non-null int64 149 | 2 Pclass 891 non-null int64 150 | 3 Name 891 non-null object 151 | 4 Sex 891 non-null object 152 | 5 Age 714 non-null float64 153 | 6 SibSp 891 non-null int64 154 | 7 Parch 891 non-null int64 155 | 8 Ticket 891 non-null object 156 | 9 Fare 891 non-null float64 157 | 10 Cabin 204 non-null object 158 | 11 Embarked 889 non-null object 159 | dtypes: float64(2), int64(5), object(5) 160 | memory usage: 83.7+ KB 161 | ``` 162 | --- 163 | ### `train_df.info()` 164 | ```markdown 165 | 166 | RangeIndex: 418 entries, 0 to 417 167 | Data columns (total 11 columns): 168 | # Column Non-Null Count Dtype 169 | --- ------ -------------- ----- 170 | 0 PassengerId 418 non-null int64 171 | 1 Pclass 418 non-null int64 172 | 2 Name 418 non-null object 173 | 3 Sex 418 non-null object 174 | 4 Age 332 non-null float64 175 | 5 SibSp 418 non-null int64 176 | 6 Parch 418 non-null int64 177 | 7 Ticket 418 non-null object 178 | 8 Fare 417 non-null float64 179 | 9 Cabin 91 non-null object 180 | 10 Embarked 418 non-null object 181 | dtypes: float64(2), int64(4), object(5) 182 | memory usage: 36.1+ KB 183 | 184 | ``` 185 | ### Tools and Libraries 186 | 187 | - **Jupyter Notebook:** Used for interactive data analysis and model development. 188 | 189 | - **Python Libraries:** 190 | - ![NumPy](https://img.shields.io/badge/-NumPy-013243?logo=numpy&logoColor=white) 191 | - ![pandas](https://img.shields.io/badge/-pandas-150458?logo=pandas&logoColor=white) 192 | - ![Matplotlib](https://img.shields.io/badge/-Matplotlib-11557c?logo=matplotlib&logoColor=white) 193 | - ![scikit-learn](https://img.shields.io/badge/-scikit--learn-F7931E?logo=scikit-learn&logoColor=white) 194 | - ![Seaborn](https://img.shields.io/badge/-Seaborn-4C72B0?logo=seaborn&logoColor=white) 195 | 196 | ### Repository Badges 197 | 198 | - ![GitHub repo size](https://img.shields.io/github/repo-size/rubypoddar/Titanic-Survival-Prediction?style=flat-square&logo=github) 199 | - ![GitHub issues](https://img.shields.io/github/issues/rubypoddar/Titanic-Survival-Prediction?style=flat-square&logo=github) 200 | - ![GitHub pull requests](https://img.shields.io/github/issues-pr/rubypoddar/Titanic-Survival-Prediction?style=flat-square&logo=github) 201 | - ![GitHub last commit](https://img.shields.io/github/last-commit/rubypoddar/Titanic-Survival-Prediction?style=flat-square&logo=github) 202 | 203 | --- 204 | ### Titanic project Structure 205 | 206 | ``` 207 | [Data Loading and Exploration] -----> [Data Cleaning] -----> [Feature Engineering] -----> [EDA] 208 | | 209 | v 210 | [Data Preprocessing] -----> [Model Definition and Training] -----> [Model Evaluation] -----> [Results Interpretation] 211 | | 212 | v 213 | [Conclusion] -----> [References and Acknowledgments] -----> [Tools and Libraries] -----> [Author] 214 | ``` 215 | --- 216 | 217 | Certainly! Here's a brief explanation of the flowchart with a placeholder for the author section: 218 | 219 | --- 220 | 221 | **[Data Loading and Exploration]** 222 | - **Purpose:** Load the Titanic dataset and explore its structure and initial insights. 223 | - **Activities:** 224 | 1. Load dataset using pandas DataFrame (`pd.read_csv()`). 225 | 2. Display the first few rows (`head()`) to inspect data. 226 | 3. Check data types, missing values, and basic statistics (`info()`, `describe()`). 227 | 228 | --- 229 | 230 | **[Data Cleaning]** 231 | - **Purpose:** Prepare dataset for analysis by handling missing values and irrelevant columns. 232 | - **Activities:** 233 | 1. Identify columns with missing values (`isnull()`). 234 | 2. Decide on imputation or removal of missing values (`fillna()`, `drop()`). 235 | 3. Remove unnecessary columns (`drop()`). 236 | 237 | --- 238 | 239 | **[Feature Engineering]** 240 | - **Purpose:** Create new features or transform existing ones to enhance predictive power. 241 | - **Activities:** 242 | 1. Extract titles from names to derive social status (`apply()` with custom function). 243 | 2. Create family size feature combining `SibSp` and `Parch`. 244 | 3. Encode categorical variables (`Sex`, `Embarked`) using OneHotEncoder. 245 | 246 | --- 247 | 248 | **[EDA (Exploratory Data Analysis)]** 249 | - **Purpose:** Visualize data distributions and relationships between variables. 250 | - **Activities:** 251 | 1. Plot histograms, box plots, and scatter plots to understand data distributions. 252 | 2. Explore correlations between features and survival outcomes (`corr()`, heatmaps). 253 | 3. Visualize survival rates across different variables (e.g., age, gender, ticket class). 254 | 255 | --- 256 | 257 | **[Data Preprocessing]** 258 | - **Purpose:** Prepare dataset for machine learning models by scaling and encoding features. 259 | - **Activities:** 260 | 1. Scale numerical features to a standard range using `StandardScaler`. 261 | 2. Encode categorical features using `OneHotEncoder`. 262 | 3. Split dataset into training and testing sets (`train_test_split`). 263 | 264 | --- 265 | 266 | **[Model Definition and Training]** 267 | - **Purpose:** Define machine learning models and train them on preprocessed data. 268 | - **Activities:** 269 | 1. Choose models (e.g., `RandomForestClassifier`, `LogisticRegression`). 270 | 2. Train models using training data (`fit()` method). 271 | 3. Optimize models through hyperparameter tuning (`GridSearchCV`, `RandomizedSearchCV`). 272 | 273 | --- 274 | 275 | **[Model Evaluation]** 276 | - **Purpose:** Evaluate the performance of trained models using various metrics. 277 | - **Activities:** 278 | 1. Calculate accuracy, precision, recall, and F1-score (`metrics` module). 279 | 2. Generate confusion matrices to visualize model performance. 280 | 3. Plot ROC curves and calculate AUC to assess model discrimination ability. 281 | 282 | --- 283 | 284 | **[Results Interpretation]** 285 | - **Purpose:** Analyze and interpret model results to understand predictive factors. 286 | - **Activities:** 287 | 1. Evaluate feature importance using model-specific attributes (`feature_importances_`). 288 | 2. Compare model performances and identify strengths and weaknesses. 289 | 3. Derive insights into factors influencing survival predictions (e.g., age, gender, class). 290 | 291 | --- 292 | ### [Conclusion] 293 | --- 294 | 295 | 296 | The Titanic Survival Prediction project delved into analyzing and predicting survival outcomes based on passenger attributes. Through comprehensive data exploration, cleaning, feature engineering, and model training, several key insights were uncovered: 297 | 298 | --- 299 | 300 | ### Key Findings 301 | --- 302 | 303 | 1. **Significant Predictors:** Gender, age, and ticket class emerged as pivotal factors influencing survival rates. Females and passengers in higher classes exhibited notably higher chances of survival, reflecting the prioritization of women and affluent individuals during the Titanic disaster. 304 | --- 305 | 2. **Model Performance:** Employing machine learning algorithms such as Random Forest and Logistic Regression yielded robust results. The models achieved an accuracy exceeding 80% on the test set, with commendable precision, recall, and F1-score metrics. This underscores their effectiveness in predicting survival outcomes. 306 | --- 307 | 308 | 3. **Limitations and Considerations:** Despite the promising outcomes, challenges like missing data and simplifications in feature engineering may affect model generalizability. Moreover, the historical context and inherent biases in the dataset necessitate careful interpretation of results. 309 | --- 310 | 311 | ### Recommendations 312 | --- 313 | 314 | - **Enhancing Model Accuracy:** Future efforts could focus on integrating additional features, exploring advanced modeling techniques (e.g., ensemble methods), and addressing data imbalances to enhance model accuracy and reliability. 315 | --- 316 | - **Real-world Application:** Deploying the model in real-time disaster simulations or historical analysis could provide practical insights into survival prediction strategies and improve emergency response protocols. 317 | --- 318 | 319 | ### Conclusion 320 | --- 321 | 322 | In summary, this project not only provided valuable insights into the factors influencing survival aboard the Titanic but also showcased the application of data science methodologies in historical analysis and predictive modeling. By acknowledging its limitations and leveraging advancements in data science, this work contributes to ongoing discussions on disaster response and risk assessment strategies. 323 | 324 | --- 325 | 326 | **[References and Acknowledgments]** 327 | 328 | **Datasets and Libraries:** 329 | - Titanic dataset: Available at [Titanic-Data-Science-Project](https://github.com/rubypoddar/Titanic-Survival-Prediction/tree/main/Titanic%20CSV). 330 | - Libraries used: Python (NumPy, pandas, Matplotlib, scikit-learn, Seaborn). 331 | 332 | **Contributions:** 333 | - Special thanks to the contributors and resources involved in the Titanic Survival Prediction project. 334 | 335 | --- 336 | 337 | 338 | 339 | **[Tools and Libraries]** 340 | - **Purpose:** List tools and libraries used in the project for transparency and reproducibility. 341 | - **Activities:** 342 | 1. Mention Jupyter Notebook, Python libraries (NumPy, pandas, Matplotlib, scikit-learn, Seaborn). 343 | 344 | --- 345 | 346 | **[Author]** 347 | - **Hello!** I am Ruby Poddar, a data science enthusiast, and I created this project to explore the Titanic dataset using various data science techniques and machine learning models. 348 | 349 | --- 350 | 351 | 352 | Sure, here's a concise installation guide for Jupyter Notebook and the required libraries (NumPy, pandas, Matplotlib, scikit-learn, Seaborn): 353 | --- 354 | 355 | ### Installation 356 | 357 | #### Jupyter Notebook 358 | 359 | 1. **Python Installation**: 360 | - Ensure Python is installed. Download and install from [python.org](https://www.python.org/downloads/) if not already installed. 361 | 362 | 2. **Install Jupyter Notebook**: 363 | - Open your command line interface (CLI). 364 | - Install Jupyter using pip, Python's package installer: 365 | 366 | ```bash 367 | pip install jupyterlab 368 | ``` 369 | 370 | - Alternatively, install Jupyter Notebook using conda if you have Anaconda or Miniconda installed: 371 | 372 | ```bash 373 | conda install -c conda-forge jupyterlab 374 | ``` 375 | 376 | 3. **Start Jupyter Notebook**: 377 | - Once installed, you can start Jupyter Notebook by entering the following command in your CLI: 378 | 379 | ```bash 380 | jupyter notebook 381 | ``` 382 | 383 | - This will open Jupyter Notebook in your default web browser. 384 | 385 | #### Libraries (NumPy, pandas, Matplotlib, scikit-learn, Seaborn) 386 | 387 | 1. **Install Required Libraries**: 388 | - Ensure you have installed all necessary libraries using pip: 389 | 390 | ```bash 391 | pip install numpy pandas matplotlib scikit-learn seaborn 392 | ``` 393 | 394 | - This command installs the following libraries: 395 | - NumPy: For numerical computing with Python. 396 | - pandas: For data manipulation and analysis. 397 | - Matplotlib: For plotting and data visualization. 398 | - scikit-learn: For machine learning algorithms and utilities. 399 | - Seaborn: For statistical data visualization. 400 | 401 | 2. **Verification**: 402 | - Verify the installation of each library by importing them in a Python environment (e.g., Jupyter Notebook) and checking for any errors: 403 | 404 | ```python 405 | import numpy 406 | import pandas 407 | import matplotlib 408 | import sklearn 409 | import seaborn 410 | ``` 411 | 412 | - If no errors occur, the libraries are successfully installed and ready to use. 413 | 414 | --- 415 | ### Repository Usage 416 | 417 | #### Cloning the Repository 418 | 419 | 1. **Clone the Repository**: 420 | - Open your command line interface (CLI). 421 | - Navigate to the directory where you want to clone the repository. 422 | - Use the following command to clone the repository: 423 | 424 | ```bash 425 | git clone https://github.com/rubypoddar/Titanic-Survival-Prediction.git 426 | ``` 427 | 428 | - This command clones the entire repository to your local machine. 429 | 430 | #### Navigating the Repository 431 | 432 | 2. **Navigate into the Repository**: 433 | - Change directory to the cloned repository: 434 | 435 | ```bash 436 | cd Titanic-Survival-Prediction 437 | ``` 438 | 439 | - Now you are inside the repository directory, ready to access its contents. 440 | 441 | #### Managing Files 442 | 443 | 3. **View Repository Files**: 444 | - List all files and directories in the repository: 445 | 446 | ```bash 447 | ls # On Unix/Linux/Mac 448 | dir # On Windows 449 | ``` 450 | 451 | - This command shows all files and directories in the current location. 452 | 453 | 4. **Open Jupyter Notebook**: 454 | - Start Jupyter Notebook to work with project files: 455 | 456 | ```bash 457 | jupyter notebook 458 | ``` 459 | 460 | - This command opens Jupyter Notebook in your default web browser. You can now navigate to `.ipynb` files and start working on your project. 461 | 462 | #### Git Commands 463 | 464 | 5. **Check Repository Status**: 465 | - View the status of your repository (e.g., untracked files, modified files): 466 | 467 | ```bash 468 | git status 469 | ``` 470 | 471 | 6. **Add and Commit Changes**: 472 | - Add all changes to the staging area: 473 | 474 | ```bash 475 | git add . 476 | ``` 477 | 478 | - Commit changes with a descriptive message: 479 | 480 | ```bash 481 | git commit -m "Add initial data exploration notebook" 482 | ``` 483 | 484 | 7. **Push Changes to GitHub**: 485 | - Push committed changes to the remote repository: 486 | 487 | ```bash 488 | git push origin main 489 | ``` 490 | 491 | - Replace `main` with the appropriate branch name if not using the default. 492 | 493 | --- 494 | ## Contact 495 | 496 | **If you have any questions, doubts, or feedback regarding the project, installation, or repository usage, please feel free to contact me via email at** **rubypoddar101@gmail.com.** 497 | 498 | #### Your input is highly valued and will contribute to the improvement of this project. Thank you for your interest and collaboration! 499 | 500 | 501 | -------------------------------------------------------------------------------- /TITANIC SURVIVAL PREDICTION/Titanic CSV/titanic.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 4 | 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 5 | 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 6 | 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 7 | 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 8 | 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 9 | 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 10 | 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 11 | 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 12 | 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S 13 | 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S 14 | 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S 15 | 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S 16 | 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S 17 | 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S 18 | 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q 19 | 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S 20 | 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S 21 | 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C 22 | 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S 23 | 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S 24 | 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q 25 | 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S 26 | 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S 27 | 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S 28 | 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C 29 | 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S 30 | 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q 31 | 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S 32 | 31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C 33 | 32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C 34 | 33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q 35 | 34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S 36 | 35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C 37 | 36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S 38 | 37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C 39 | 38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S 40 | 39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S 41 | 40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C 42 | 41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S 43 | 42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S 44 | 43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C 45 | 44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C 46 | 45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q 47 | 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S 48 | 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q 49 | 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q 50 | 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C 51 | 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S 52 | 51,0,3,"Panula, Master. 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