minmaxscaler

ML / DL Algorithms implemented from scratch. Developed with only numpy as dependency. Machine Learning Algorithms such as Support Vector Machine, Linear Regression, Artificial Neural Networks and other data transformation algorithms are implemented. Project is released as a python package and can be download from Python Package Installer.

This is a project to detect anomalies in pump sensor data using One-Class Support Vector Machines (SVM). The data is preprocessed by dropping columns with missing values and scaled using MinMaxScaler. The one-class SVM classifier is trained and used to predict anomalies in the data, which are then saved in a new file "results.csv".

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.

Supervised learning techniques, focusing on linear models, regularization, overfitting, underfitting, and metrics for evaluating model quality

Build a machine learning model to predict if a credit card application will get approved.

I have created this project as a part of virtual internship programme in data Science.

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.

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

The sixth project from a Data Scientist with Python track by DataCamp

Sales Analysis using Python

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.

An innovative system for filtering and categorizing movie reviews

A full-stack Swiggy sales analytics project analyzing 197,430 food delivery orders across India. Built three original frameworks — City Expansion Index, Menu Intelligence Matrix, and Restaurant Health Score — to answer real growth strategy questions.

Exploratory Data Analysis Part-1

Time-series stock price forecasting pipeline using LSTM neural networks and Scikit-learn — automated feature engineering with 21 technical indicators, rolling-window validation, and interactive Plotly dashboards for volatility and investment insights.

Customer churn prediction using deep learning

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.

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

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.