General Code Regression

Welcome to the General Regression Code repository! This code file provides a versatile and flexible foundation for performing regression analysis on a wide range of datasets. Whether you're working on predicting sales, housing prices, or any other continuous outcome, this code offers a comprehensive toolkit to facilitate your regression tasks.

Overview

• Data Loading: Easily load your dataset in various formats (CSV, Excel, etc.).
• Data Preprocessing: Handle missing values, categorical variables, and feature scaling.
• Exploratory Data Analysis (EDA): Gain insights into your data's distribution, correlations, and more.
• Feature Selection: Choose relevant features using techniques like correlation and feature importance.
• Model Selection: Select from a variety of regression models, including Linear Regression, Random Forest Regression, and more.
• Hyperparameter Tuning: Fine-tune model parameters for optimal performance using grid search or random search.
• Model Evaluation: Assess model performance using metrics like Mean Squared Error (MSE), R-squared, and R2 Score, etc.
• Prediction: Generate predictions on new data using trained regression models.
• Visualization: Visualize regression results, feature importance, and more.

Data Preprocessing

1. Importing libraries and dataset
2. Handling missing values
3. Handling duplicate values
4. Handling outliers
5. Dealing with Text-based data
6. Dealing with Date time data

Exploratory Data Analysis

1. Univariate analysis
2. Bi-Variate Analysis
3. Multi-variate Analysis

Model Preprocessing

1. Encoding categorical Columns using one-hot encoding, label encoding, Get Dummies
2. User-defined function for model evaluation
   • Fit model
   • Print train score, test score, precision score, Recall score,
   • Print predictions
   • Print confusion matrix
   • Print Classification report
3. User-defined function for plotting ROC curve
4. User-defined function for comparing all models.
5. Select dependent and independent feature
6. Split train test data

Model building

1. Linear Regression
   • Regularisation technniques like Lasso, Ridge, ElasticNet
2. DecisionTree Regression
3. Random Forest Regression
4. Support Vector Regression
5. KNN Regression
6. XGBoost Regressor

Contribution

Contributions are encouraged! If you have ideas for improving the codebase, adding more regression algorithms, or enhancing the visualization capabilities, please submit a pull request following the guidelines.

By utilizing this versatile regression code, you can streamline your regression analysis tasks and gain valuable insights from your data. Whether you're a beginner or an experienced data scientist, this repository provides a solid starting point for your regression projects. Happy coding!