├── LICENSE.txt ├── README.md ├── Titanic CSV ├── titanic.csv ├── titanic_test.csv └── train.csv └── Titanic Data Science Project └── titanic project.ipynb /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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![image](https://see.fontimg.com/api/rf5/lg61w/ZDBmZjBhMDEzYjdkNDcwYjk5ZTI2NTE3M2I1NWZjNzYudHRm/VGl0YW5pYyBTdXJ2aXZhbCBQcmVkaWN0aW9u/palm-leaf-demo-regular.png?r=fs&h=31&w=1250&fg=7EFD2D&bg=FFFFFF&tb=1&s=25) 2 | 3 | ## Titanic Survival Prediction 4 | 5 | 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. 6 | 7 | --- 8 | ![Titanic](https://i.pinimg.com/564x/98/73/c6/9873c68f08671ca72aece2d1ceb6b93b.jpg) 9 | --- 10 | 11 | ### Repository Structure 12 | 13 | ``` 14 | ├── Titanic_CSV/ 15 | │ ├── titanic.csv # Complete Titanic dataset 16 | │ ├── titanic_train.csv # Training dataset 17 | │ ├── titanic_test.csv # Testing dataset 18 | ├── Titanic_Data_Science_Project/ 19 | │ ├── titanic_project.ipynb # Jupyter notebook containing the project code 20 | ├── LICENSE.txt # License information for the project 21 | └──README.md # Overview and description of the project 22 | ``` 23 | 24 | #### Explanation 25 | 26 | - **Titanic_CSV/**: This directory contains all the datasets used in the project. 27 | - **titanic.csv**: The complete Titanic dataset with all the available features. 28 | - **titanic_train.csv**: The training dataset, which includes features and the target variable (survival) used to train the machine learning models. 29 | - **titanic_test.csv**: The testing dataset, which includes features but not the target variable, used to evaluate the performance of the trained models. 30 | 31 | - **Titanic_Data_Science_Project/**: This directory contains the Jupyter notebook with the project code and analysis. 32 | - **titanic_project.ipynb**: The main Jupyter notebook containing all the code for data loading, preprocessing, feature engineering, model training, evaluation, and result interpretation. 33 | 34 | - **LICENSE**: The file that contains the license information for the project, specifying the terms under which the project can be used and distributed. 35 | 36 | - **README.md**: The readme file that provides an overview and description of the project, instructions for running the code, and any other relevant information. 37 | 38 | --- 39 | # Introduction 40 | 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. 41 | 42 | 43 | --- 44 | 45 | ### Tools: 46 | - Jupyter Notebook 47 | 48 | ### Libraries: 49 | - **Numpy**: For numerical computing with arrays and matrices. 50 | - **pandas**: For data manipulation and analysis. 51 | - **Matplotlib**: For creating static, animated, and interactive visualizations in Python. 52 | - **scikit-learn**: For machine learning algorithms, preprocessing, model selection, and evaluation. 53 | - **seaborn**: For statistical data visualization based on Matplotlib, providing a high-level interface for drawing attractive and informative statistical graphics. 54 | 55 | 56 | --- 57 | 58 | # Data Exploration 59 | 60 | 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. 61 | 62 | ### `titanic_df.head(10)` 63 | 64 | --- 65 | | | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | 66 | |---|-------------|----------|--------|------|-----|-----|-------|-------|--------|------|-------|----------| 67 | | 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | 68 | | 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | 69 | | 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 70 | | 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | 71 | | 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S | 72 | | 5 | 6 | 0 | 3 | Moran, Mr. James | male | NaN | 0 | 0 | 330877 | 8.4583 | NaN | Q | 73 | | 6 | 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54.0 | 0 | 0 | 17463 | 51.8625 | E46 | S | 74 | | 7 | 8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2.0 | 3 | 1 | 349909 | 21.0750 | NaN | S | 75 | | 8 | 9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27.0 | 0 | 2 | 347742 | 11.1333 | NaN | S | 76 | | 9 | 10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14.0 | 1 | 0 | 237736 | 30.0708 | NaN | C | 77 | 78 | ---- 79 | 80 | ### `train_df` 81 | 82 | 83 | --- 84 | 85 | | PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | 86 | |--------------:|---------:|:----------------------------------------------|:-------|------:|--------:|--------:|:---------|--------:|:--------|:-----------| 87 | | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q | 88 | | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47 | 1 | 0 | 363272 | 7 | NaN | S | 89 | | 894 | 2 | Myles, Mr. Thomas Francis | male | 62 | 0 | 0 | 240276 | 9.6875 | NaN | Q | 90 | | 895 | 3 | Wirz, Mr. Albert | male | 27 | 0 | 0 | 315154 | 8.6625 | NaN | S | 91 | | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22 | 1 | 1 | 3101298 | 12.2875 | NaN | S | 92 | | 897 | 3 | Svensson, Mr. Johan Cervin | male | 14 | 0 | 0 | 7538 | 9.225 | NaN | S | 93 | | 898 | 3 | Connolly, Miss. Kate | female | 30 | 0 | 0 | 330972 | 7.6292 | NaN | Q | 94 | | 899 | 2 | Caldwell, Mr. Albert Francis | male | 26 | 1 | 1 | 248738 | 29 | NaN | S | 95 | | 900 | 3 | Abrahim, Mrs. Joseph (Sophie Halaut Easu) | female | 18 | 0 | 0 | 2657 | 7.2292 | NaN | C | 96 | | 901 | 3 | Davies, Mr. John Samuel | male | 21 | 2 | 0 | A/4 48871| 24.15 | NaN | S | 97 | 98 | 99 | --- 100 | 101 | ### `titanic_df.describe()` 102 | 103 | --- 104 | 105 | | | PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | 106 | |----------|-------------|----------|--------|----------|----------|----------|-----------| 107 | | count | 891.000000 | 891.000000 | 891.000000 | 714.000000 | 891.000000 | 891.000000 | 891.000000 | 108 | | mean | 446.000000 | 0.383838 | 2.308642 | 29.699118 | 0.523008 | 0.381594 | 32.204208 | 109 | | std | 257.353842 | 0.486592 | 0.836071 | 14.526497 | 1.102743 | 0.806057 | 49.693429 | 110 | | min | 1.000000 | 0.000000 | 1.000000 | 0.420000 | 0.000000 | 0.000000 | 0.000000 | 111 | | 25% | 223.500000 | 0.000000 | 2.000000 | 20.125000 | 0.000000 | 0.000000 | 7.910400 | 112 | | 50% | 446.000000 | 0.000000 | 3.000000 | 28.000000 | 0.000000 | 0.000000 | 14.454200 | 113 | | 75% | 668.500000 | 1.000000 | 3.000000 | 38.000000 | 1.000000 | 0.000000 | 31.000000 | 114 | | max | 891.000000 | 1.000000 | 3.000000 | 80.000000 | 8.000000 | 6.000000 | 512.329200 | 115 | 116 | 117 | --- 118 | 119 | ### `train_df.describe()` 120 | 121 | --- 122 | 123 | | | PassengerId | Pclass | Age | SibSp | Parch | Fare | 124 | |----------|-------------|-----------|-----------|-----------|-----------|------------| 125 | | count | 418.000000 | 418.000000 | 332.000000 | 418.000000 | 418.000000 | 417.000000 | 126 | | mean | 1100.500000 | 2.265550 | 30.272590 | 0.447368 | 0.392344 | 35.627188 | 127 | | std | 120.810458 | 0.841838 | 14.181209 | 0.896760 | 0.981429 | 55.907576 | 128 | | min | 892.000000 | 1.000000 | 0.170000 | 0.000000 | 0.000000 | 0.000000 | 129 | | 25% | 996.250000 | 1.000000 | 21.000000 | 0.000000 | 0.000000 | 7.895800 | 130 | | 50% | 1100.500000 | 3.000000 | 27.000000 | 0.000000 | 0.000000 | 14.454200 | 131 | | 75% | 1204.750000 | 3.000000 | 39.000000 | 1.000000 | 0.000000 | 31.500000 | 132 | | max | 1309.000000 | 3.000000 | 76.000000 | 8.000000 | 9.000000 | 512.329200 | 133 | 134 | 135 | 136 | --- 137 | 138 | ### `titanic_df.info()` 139 | ```markdown 140 | 141 | RangeIndex: 891 entries, 0 to 890 142 | Data columns (total 12 columns): 143 | # Column Non-Null Count Dtype 144 | --- ------ -------------- ----- 145 | 0 PassengerId 891 non-null int64 146 | 1 Survived 891 non-null int64 147 | 2 Pclass 891 non-null int64 148 | 3 Name 891 non-null object 149 | 4 Sex 891 non-null object 150 | 5 Age 714 non-null float64 151 | 6 SibSp 891 non-null int64 152 | 7 Parch 891 non-null int64 153 | 8 Ticket 891 non-null object 154 | 9 Fare 891 non-null float64 155 | 10 Cabin 204 non-null object 156 | 11 Embarked 889 non-null object 157 | dtypes: float64(2), int64(5), object(5) 158 | memory usage: 83.7+ KB 159 | ``` 160 | --- 161 | ### `train_df.info()` 162 | ```markdown 163 | 164 | RangeIndex: 418 entries, 0 to 417 165 | Data columns (total 11 columns): 166 | # Column Non-Null Count Dtype 167 | --- ------ -------------- ----- 168 | 0 PassengerId 418 non-null int64 169 | 1 Pclass 418 non-null int64 170 | 2 Name 418 non-null object 171 | 3 Sex 418 non-null object 172 | 4 Age 332 non-null float64 173 | 5 SibSp 418 non-null int64 174 | 6 Parch 418 non-null int64 175 | 7 Ticket 418 non-null object 176 | 8 Fare 417 non-null float64 177 | 9 Cabin 91 non-null object 178 | 10 Embarked 418 non-null object 179 | dtypes: float64(2), int64(4), object(5) 180 | memory usage: 36.1+ KB 181 | 182 | ``` 183 | ### Tools and Libraries 184 | 185 | - **Jupyter Notebook:** Used for interactive data analysis and model development. 186 | 187 | - **Python Libraries:** 188 | - ![NumPy](https://img.shields.io/badge/-NumPy-013243?logo=numpy&logoColor=white) 189 | - ![pandas](https://img.shields.io/badge/-pandas-150458?logo=pandas&logoColor=white) 190 | - ![Matplotlib](https://img.shields.io/badge/-Matplotlib-11557c?logo=matplotlib&logoColor=white) 191 | - ![scikit-learn](https://img.shields.io/badge/-scikit--learn-F7931E?logo=scikit-learn&logoColor=white) 192 | - ![Seaborn](https://img.shields.io/badge/-Seaborn-4C72B0?logo=seaborn&logoColor=white) 193 | 194 | ### Repository Badges 195 | 196 | - ![GitHub repo size](https://img.shields.io/github/repo-size/rubypoddar/Titanic-Survival-Prediction?style=flat-square&logo=github) 197 | - ![GitHub issues](https://img.shields.io/github/issues/rubypoddar/Titanic-Survival-Prediction?style=flat-square&logo=github) 198 | - ![GitHub pull requests](https://img.shields.io/github/issues-pr/rubypoddar/Titanic-Survival-Prediction?style=flat-square&logo=github) 199 | - ![GitHub last commit](https://img.shields.io/github/last-commit/rubypoddar/Titanic-Survival-Prediction?style=flat-square&logo=github) 200 | 201 | --- 202 | ### Titanic project Structure 203 | 204 | ``` 205 | [Data Loading and Exploration] -----> [Data Cleaning] -----> [Feature Engineering] -----> [EDA] 206 | | 207 | v 208 | [Data Preprocessing] -----> [Model Definition and Training] -----> [Model Evaluation] -----> [Results Interpretation] 209 | | 210 | v 211 | [Conclusion] -----> [References and Acknowledgments] -----> [Tools and Libraries] -----> [Author] 212 | ``` 213 | --- 214 | 215 | Certainly! Here's a brief explanation of the flowchart with a placeholder for the author section: 216 | 217 | --- 218 | 219 | **[Data Loading and Exploration]** 220 | - **Purpose:** Load the Titanic dataset and explore its structure and initial insights. 221 | - **Activities:** 222 | 1. Load dataset using pandas DataFrame (`pd.read_csv()`). 223 | 2. Display the first few rows (`head()`) to inspect data. 224 | 3. Check data types, missing values, and basic statistics (`info()`, `describe()`). 225 | 226 | --- 227 | 228 | **[Data Cleaning]** 229 | - **Purpose:** Prepare dataset for analysis by handling missing values and irrelevant columns. 230 | - **Activities:** 231 | 1. Identify columns with missing values (`isnull()`). 232 | 2. Decide on imputation or removal of missing values (`fillna()`, `drop()`). 233 | 3. Remove unnecessary columns (`drop()`). 234 | 235 | --- 236 | 237 | **[Feature Engineering]** 238 | - **Purpose:** Create new features or transform existing ones to enhance predictive power. 239 | - **Activities:** 240 | 1. Extract titles from names to derive social status (`apply()` with custom function). 241 | 2. Create family size feature combining `SibSp` and `Parch`. 242 | 3. Encode categorical variables (`Sex`, `Embarked`) using OneHotEncoder. 243 | 244 | --- 245 | 246 | **[EDA (Exploratory Data Analysis)]** 247 | - **Purpose:** Visualize data distributions and relationships between variables. 248 | - **Activities:** 249 | 1. Plot histograms, box plots, and scatter plots to understand data distributions. 250 | 2. Explore correlations between features and survival outcomes (`corr()`, heatmaps). 251 | 3. Visualize survival rates across different variables (e.g., age, gender, ticket class). 252 | 253 | --- 254 | 255 | **[Data Preprocessing]** 256 | - **Purpose:** Prepare dataset for machine learning models by scaling and encoding features. 257 | - **Activities:** 258 | 1. Scale numerical features to a standard range using `StandardScaler`. 259 | 2. Encode categorical features using `OneHotEncoder`. 260 | 3. Split dataset into training and testing sets (`train_test_split`). 261 | 262 | --- 263 | 264 | **[Model Definition and Training]** 265 | - **Purpose:** Define machine learning models and train them on preprocessed data. 266 | - **Activities:** 267 | 1. Choose models (e.g., `RandomForestClassifier`, `LogisticRegression`). 268 | 2. Train models using training data (`fit()` method). 269 | 3. Optimize models through hyperparameter tuning (`GridSearchCV`, `RandomizedSearchCV`). 270 | 271 | --- 272 | 273 | **[Model Evaluation]** 274 | - **Purpose:** Evaluate the performance of trained models using various metrics. 275 | - **Activities:** 276 | 1. Calculate accuracy, precision, recall, and F1-score (`metrics` module). 277 | 2. Generate confusion matrices to visualize model performance. 278 | 3. Plot ROC curves and calculate AUC to assess model discrimination ability. 279 | 280 | --- 281 | 282 | **[Results Interpretation]** 283 | - **Purpose:** Analyze and interpret model results to understand predictive factors. 284 | - **Activities:** 285 | 1. Evaluate feature importance using model-specific attributes (`feature_importances_`). 286 | 2. Compare model performances and identify strengths and weaknesses. 287 | 3. Derive insights into factors influencing survival predictions (e.g., age, gender, class). 288 | 289 | --- 290 | ### [Conclusion] 291 | --- 292 | 293 | 294 | 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: 295 | 296 | --- 297 | 298 | ### Key Findings 299 | --- 300 | 301 | 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. 302 | --- 303 | 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. 304 | --- 305 | 306 | 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. 307 | --- 308 | 309 | ### Recommendations 310 | --- 311 | 312 | - **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. 313 | --- 314 | - **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. 315 | --- 316 | 317 | ### Conclusion 318 | --- 319 | 320 | 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. 321 | 322 | --- 323 | 324 | **[References and Acknowledgments]** 325 | 326 | **Datasets and Libraries:** 327 | - Titanic dataset: Available at [Titanic-Data-Science-Project](https://github.com/rubypoddar/Titanic-Survival-Prediction/tree/main/Titanic%20CSV). 328 | - Libraries used: Python (NumPy, pandas, Matplotlib, scikit-learn, Seaborn). 329 | 330 | **Contributions:** 331 | - Special thanks to the contributors and resources involved in the Titanic Survival Prediction project. 332 | 333 | --- 334 | 335 | 336 | 337 | **[Tools and Libraries]** 338 | - **Purpose:** List tools and libraries used in the project for transparency and reproducibility. 339 | - **Activities:** 340 | 1. Mention Jupyter Notebook, Python libraries (NumPy, pandas, Matplotlib, scikit-learn, Seaborn). 341 | 342 | --- 343 | 344 | **[Author]** 345 | - **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. 346 | 347 | --- 348 | 349 | 350 | Sure, here's a concise installation guide for Jupyter Notebook and the required libraries (NumPy, pandas, Matplotlib, scikit-learn, Seaborn): 351 | --- 352 | 353 | ### Installation 354 | 355 | #### Jupyter Notebook 356 | 357 | 1. **Python Installation**: 358 | - Ensure Python is installed. Download and install from [python.org](https://www.python.org/downloads/) if not already installed. 359 | 360 | 2. **Install Jupyter Notebook**: 361 | - Open your command line interface (CLI). 362 | - Install Jupyter using pip, Python's package installer: 363 | 364 | ```bash 365 | pip install jupyterlab 366 | ``` 367 | 368 | - Alternatively, install Jupyter Notebook using conda if you have Anaconda or Miniconda installed: 369 | 370 | ```bash 371 | conda install -c conda-forge jupyterlab 372 | ``` 373 | 374 | 3. **Start Jupyter Notebook**: 375 | - Once installed, you can start Jupyter Notebook by entering the following command in your CLI: 376 | 377 | ```bash 378 | jupyter notebook 379 | ``` 380 | 381 | - This will open Jupyter Notebook in your default web browser. 382 | 383 | #### Libraries (NumPy, pandas, Matplotlib, scikit-learn, Seaborn) 384 | 385 | 1. **Install Required Libraries**: 386 | - Ensure you have installed all necessary libraries using pip: 387 | 388 | ```bash 389 | pip install numpy pandas matplotlib scikit-learn seaborn 390 | ``` 391 | 392 | - This command installs the following libraries: 393 | - NumPy: For numerical computing with Python. 394 | - pandas: For data manipulation and analysis. 395 | - Matplotlib: For plotting and data visualization. 396 | - scikit-learn: For machine learning algorithms and utilities. 397 | - Seaborn: For statistical data visualization. 398 | 399 | 2. **Verification**: 400 | - Verify the installation of each library by importing them in a Python environment (e.g., Jupyter Notebook) and checking for any errors: 401 | 402 | ```python 403 | import numpy 404 | import pandas 405 | import matplotlib 406 | import sklearn 407 | import seaborn 408 | ``` 409 | 410 | - If no errors occur, the libraries are successfully installed and ready to use. 411 | 412 | --- 413 | ### Repository Usage 414 | 415 | #### Cloning the Repository 416 | 417 | 1. **Clone the Repository**: 418 | - Open your command line interface (CLI). 419 | - Navigate to the directory where you want to clone the repository. 420 | - Use the following command to clone the repository: 421 | 422 | ```bash 423 | git clone https://github.com/rubypoddar/Titanic-Survival-Prediction.git 424 | ``` 425 | 426 | - This command clones the entire repository to your local machine. 427 | 428 | #### Navigating the Repository 429 | 430 | 2. **Navigate into the Repository**: 431 | - Change directory to the cloned repository: 432 | 433 | ```bash 434 | cd Titanic-Survival-Prediction 435 | ``` 436 | 437 | - Now you are inside the repository directory, ready to access its contents. 438 | 439 | #### Managing Files 440 | 441 | 3. **View Repository Files**: 442 | - List all files and directories in the repository: 443 | 444 | ```bash 445 | ls # On Unix/Linux/Mac 446 | dir # On Windows 447 | ``` 448 | 449 | - This command shows all files and directories in the current location. 450 | 451 | 4. **Open Jupyter Notebook**: 452 | - Start Jupyter Notebook to work with project files: 453 | 454 | ```bash 455 | jupyter notebook 456 | ``` 457 | 458 | - This command opens Jupyter Notebook in your default web browser. You can now navigate to `.ipynb` files and start working on your project. 459 | 460 | #### Git Commands 461 | 462 | 5. **Check Repository Status**: 463 | - View the status of your repository (e.g., untracked files, modified files): 464 | 465 | ```bash 466 | git status 467 | ``` 468 | 469 | 6. **Add and Commit Changes**: 470 | - Add all changes to the staging area: 471 | 472 | ```bash 473 | git add . 474 | ``` 475 | 476 | - Commit changes with a descriptive message: 477 | 478 | ```bash 479 | git commit -m "Add initial data exploration notebook" 480 | ``` 481 | 482 | 7. **Push Changes to GitHub**: 483 | - Push committed changes to the remote repository: 484 | 485 | ```bash 486 | git push origin main 487 | ``` 488 | 489 | - Replace `main` with the appropriate branch name if not using the default. 490 | 491 | --- 492 | ## Contact 493 | 494 | **If you have any questions, doubts, or feedback regarding the project, installation, or repository usage, please feel free to contact me via email at** **rubypoddarr@gmail.com.** 495 | 496 | #### Your input is highly valued and will contribute to the improvement of this project. Thank you for your interest and collaboration! 497 | 498 | 499 | -------------------------------------------------------------------------------- /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. 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