A machine learning project for customer segmentation using the DBSCAN density-based clustering algorithm on the Wholesale Customers dataset. The project identifies purchasing patterns, detects outliers, analyzes customer behavior, and visualizes cluster differences across spending categories.

Sales Analysis using Python

Repository for deployment with Streamlit

A project showcasing the use of machine learning in detecting and classifying electrical faults

This project demonstrates how to build a Long Short-Term Memory (LSTM) model using Keras to predict Tesla's stock closing prices based on historical data. Import libraries pandas, NumPy, to load and preprocess Titanic data, scale features, and prepare for machine learning or visualization.

Binary classification model research

End-to-end time series forecasting using both Machine Learning and Deep Learning models. Includes data preprocessing, EDA, feature scaling, and performance evaluation on real-world datasets.

The project checks with the users features and then predicts the output

AMUSIC is an AI-driven music recommendation system that helps users discover personalized songs. Using Python, Streamlit, and Scikit-learn, it offers smart recommendations, advanced search, and interactive music insights. Users can save favorites, create playlists, and export data for a seamless music discovery experience.

This project predicts gold prices based on historical market data using Bi-LSTM. The model is trained with price and volume features, and evaluated using MAPE to measure prediction accuracy.

This project predicts whether a person survived the Titanic disaster based on various features using machine learning. It utilizes pipelines, ColumnTransformer, and model serialization for efficient processing and prediction.

Utilizing San Francisco crime data to identify hotspots using K-means clustering techniques.

Python Data Analysis and Visualizations

The main objective of this project is to design and implement a robust data preprocessing system that addresses common challenges such as missing values, outliers, inconsistent formatting, and noise. By performing effective data preprocessing, the project aims to enhance the quality, reliability, and usefulness of the data for machine learning.

Analyzing and predicting the stock prices,multiple machine learning models, including LSTM (Long Short-Term Memory), Prophet, and others

An innovative system for filtering and categorizing movie reviews

Data Science Project: Comparing 3 Deep Learning Methods (CNN, LSTM, and Transfer Learning).

The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.