Credit Card Approval Prediction based on users' historic data.

Repository for deployment with Streamlit

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 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.

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.

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

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.

Customer churn prediction using deep learning

Binary classification model research

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

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.

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

Performing kmeans clustering and also providing elbow plot

RFM analysis focuses on identifying and segmenting customers based on their purchasing behavior. Analyzed to understand and interact with customers. It can be used together for more effective marketing and customer management strategies.

The Bike Sharing Company wants to understand the independent variables on their past data to analyze and create a machine learning model to understand the demand of the bike and accordingly plan a business strategy.

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

Anomaly Detection Using Gaussian Mixture Model

A Book Recommendation System that utilizes Python libraries such as numpy, pandas, seaborn, and matplotlib to recommend books based on user input.