Notebook for weekly project deliverables

This Repository contains some of my tensorflow predictions models on datasets like MNIST Handwritten digits, CIFAR datasets etc.

View notebooks via this link:

this notebook is about predicting the bank insurance, either the customer will take insurance or not.

Machine Learning projects with Python and Jupyter Notebook

Machine learning projects and simple model optimization notebook

Notebook with a machine learning solution to predict apartment prices

NBA players clustering and Points prediction

Neural Networks with TensorFlow 2 and Keras in Python (Jupyter notebooks included)

These notebooks contain advanced analysis of ML models of different kind of datasets

A notebook with core concepts of gradient descent algorithm to predict the prices for houses in Boston

This Jupyter Notebook implements Bayesian modeling techniques to fit a posterior distribution and forecast demand for an e-commerce company.

The notebook here consists of the concept of "Moore Penrose PseudoInverse" which approximates the inverse of a matrix and we use it to find the weight vectors in any given linear regression problem.

This repository is a training about Data Analysis using Python and Jupyter notebooks, following the exercises proposed in the 'Applied Data Science with Python' IBM certification.

This project consists of a comprehensive Jupyter Notebook where I've implemented a Linear Regression model entirely from scratch to predict taxi fares in New York City.

Classification of Breast Cancer diagnosis Using Support Vector Machines

This repo has a notebook that I worked on for making a fraud detection model. The dataset was Highly imbalanced, so i used random under sampling to balance the data.

A Streamlit application for predicting handwritten digits using a trained machine learning model. This project includes the necessary code to run the Streamlit app and train the model using a Jupyter notebook.

An analysis on Bitocin Prices, it includes two price prediction models to compare the sensibility of Bitcoin to underlying factors.

This notebook explores and analyzes the Heart Disease UCI dataset using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn. It includes data visualization, feature engineering, model building using Random Forest Classifier, and evaluation of the model's performance in predicting the presence or absence of heart disease.