This project analyzes the relationship between bike station availability and characteristics of nearby Points of Interest (POIs) using data from the CityBikes, Foursquare, and Yelp APIs. The goal is to identify key predictors of bike availability through regression modeling.

I have created a book recommender system that recommends similar books to the reader based on his/her interest. This project shows results of collaborative and content-based filtering of the given dataset.

This project aims to predict (and prevent) customer churn in the telecom industry with our advanced analytics and machine Learning.

Analyzing and predicting Google's stock prices through detailed data exploration and advanced LSTM models. This project involves data preprocessing, creating time-series sequences, constructing and training LSTM networks, and evaluating their performance to forecast future stock prices utilizing Python and Machine Learning libraries.

Sentiment analysis on Yelp reviews using NLP techniques and machine learning models. This project includes data preprocessing, feature extraction, model building, and evaluation.

This project focuses on analyzing customer churn data from a telecom company. The analysis involves various stages of data manipulation, visualization, and machine learning to predict whether a customer is likely to churn based on their demographic information, service usage, and billing details.

To analyze the provided cancer.csv data and predict whether or not a patient has breast cancer using ensemble techniques

In this project I did a thorough analysis of the Email Campaign dataset where my primary goal is to develop a machine learning model to identify and monitor the mail that is read, acknowledged, and ignored.

The Heart Disease Prediction project aims to predict the likelihood of heart disease using machine learning techniques.

Web platform for the design and execution of deep learning models for learning and initiation in the study of deep learning models.

This repo contains 3 EDA projects, 1. USA_House_Price_predictor. 2. Airline_EDA. 3. Iris_data_EDA

Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regressi

PyR@TE 3

This provides a solution for a classification problem by determining whether the customer is suitable to be approved with a credit card or not and provides another solution using the Market Basket Analysis in order to find out combinations of items that occur together frequently in transactions.

This GitHub repository contains a comprehensive project demonstrating image classification using TensorFlow and Keras on the CIFAR-10 dataset. The project covers various aspects of the machine learning pipeline, including data preprocessing, model building, training, evaluation, and visualization.

This GitHub repository contains a comprehensive project demonstrating image classification using TensorFlow and Keras on the CIFAR-10 dataset. The project covers various aspects of the machine learning pipeline, including data preprocessing, model building, training, evaluation, and visualization.

Customer buying behaviour prediction on British Airways Airline for travel.Part of Virtual internship task

Practices and Assignments from the Data Analysis and Regression Class

In this project Utilizing advanced time series forecasting models, successfully predicted department-wide sales for each store for the upcoming year and Visualizing the data in streamlit GUI.