data-preprocessing-and-cleaning

Embark on a transformative "100 Days of Machine Learning" journey. This curated repository guides enthusiasts through a hands-on approach, covering fundamental ML concepts, algorithms, and applications. Each day, engage in theoretical insights, practical coding exercises, and real-world projects. Balance theory with hands-on experience.

This is a Movie Recommendation System that suggests movies to users based on their preferences. The system uses machine learning techniques to recommend similar movies.

A small library with Pandas-like api used for function ops execution and data transforms.

In this ML project i have used Natural language processing (NLP) techniques and other data preprocessing techniques to feed my Machine Learning Algorithm a good data, and deploy it using flask.

Create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.

Improving credit risk model using Machine learning techniques. We use a host of ml models and neural network to solve the issue.

a impactful repository of predicting , analyzing the real-time groundwater levels , allowing simplified and modern way for researchers to analyze ground water levels .

This repository contains some laboratory work (TPs), mini-projects, and experimental implementations completed during my Master's degree in Artificial Intelligence (2024–2026).

An end-to-end ML project that predicts airline passenger satisfaction using a Random Forest Classifier. Covers EDA, preprocessing, model training (96% accuracy), and a live Streamlit app with 22 flight experience features.

A machine learning web app that predicts annual medical insurance charges from a patient's age, BMI, smoking status, and other demographics using a scikit-learn Random Forest pipeline.

This is a simple ANN deep learning classification technique

A project on visualizing and analyzing sales, revenue, and profit data across different markets from 2017 to 2020(India).

A collection of hands-on solutions for checkpoints in a machine learning course. Covers core ML concepts such as data preprocessing, model training, and evaluation using Scikit-learn, with practical implementation on various datasets.

Book Recommendation System using popularity ranking and item-based collaborative filtering (cosine similarity) on the Book-Crossing dataset, with a Streamlit web app.

End-to-end XGBoost regression pipeline for predicting California housing prices, with a Streamlit web app for real-time predictions.

AI-powered stock insights for Indian (NSE) stocks. Features machine learning predictions with 99 technical + fundamental features, real-time data from Yahoo Finance, and a modern React frontend.

A Machine Learning web application that predicts a student's Maths score based on demographic and academic input features.

A CNN-based handwritten digit recognition system trained on the MNIST dataset achieving 99% accuracy, deployed using Streamlit for real-time digit prediction.

E-commerce Return Rate Reduction Analysis – Data-driven project using SQL, Python (Logistic Regression), and Power BI to analyze return patterns, predict customer behavior, and provide actionable insights to reduce product returns.

This repository contains a Data Engineering and Data Cleaning Experiment project focused on preparing messy raw datasets for structured analysis and analytics. The project includes tools and scripts to load, inspect, clean, transform, and validate data using Python and SQL,helping build a reusable workflow for data preprocessing