Includes most of the contents related to Machine learning like Numpy, Pandas, Matplotlib, Linear Regression, MultiVariable Regression, Gradient Descent, Feature Scaling, Logistic Regression, Classification Measures, Decision Trees, Random Forests, Naive Bayes, KNN, SVM, PCA, NLP, Neural Networks and various projects on Gradient Descent, Logistic Regression, Decision Tree, Text Classification, etc...

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It contain my data science work from Linear Regression, Multivariable Regression and Gradient descent

Implementation and Comparative Analysis of Multivariable Linear Regression using Scikit-learn and From-Scratch Gradient Descent Approach.

Multivariable Linear Regression implemented from scratch using NumPy with Gradient Descent optimization and Random Search hyperparameter tuning.

Build and compare linear regression models manually (with gradient descent) and automatically (with Scikit-Learn). Includes simple and multivariable regression with data visualization and evaluation metrics.

An implementation of multivariable linear regression on the Boston housing dataset, using both gradient descent and the normal equation to predict median home values from selected features.

This project implements multivariable linear regression from scratch using Gradient Descent to predict insurance charges. The goal was not just to get predictions, but to deeply understand the mathematics, optimization, and data preprocessing behind linear regression, and to validate the implementation against sklearn.

Data Cleaning and Pre-Processing, Data Exploration and Visualization, Linear and Multivariable Regression, Optimisation Algorithms and Gradient Descent, Naive Bayes Classification, Descriptive Statistics and Probability Theory, Neural Networks and Deep Learning, Model Evaluation and Analysis, Serving a Tensorflow Model

Implements multivariable linear regression using gradient descent. Generates synthetic data with 3 features, fits a model to predict output, and tracks error over time. Visualizes training data and cost curve to show model learning progress. All logic is built from scratch using NumPy.