Always know what to expect from your data.

In this machine learning project, we will make use of K-means clustering which is the essential algorithm for clustering unlabeled dataset.

Data Explorer - Profile Report and EDA App

Machine Learning project that analyzed Yelp dataset to train models for recommendations using ALS and generating user rating predictions

LetsGrowMore DataScience Internship: During this Internship, I have worked on project related to Data Analytics field in which LSTM, Decision Tree, Random Forest and XGboost have been used.

FLIX-HUB is a movie recommendation system utilizing the Netflix dataset. It features comprehensive data preprocessing and analysis, generating personalized movie and TV show suggestions based on TF-IDF vectorization and cosine similarity. The project includes interactive visualizations for insights into content trends and distributions.

A comprehensive analysis of the Titanic dataset that involves data preprocessing, exploratory data analysis (EDA), feature engineering, and the development of a predictive model using machine learning techniques.

Illustration on merging of multiple datasets for implementing machine learning algorithms

Health care analytics to predict the occurrence of cardio vascular diseases

This repository contains the Jupyter notebooks explaining Exploratory data analysis & Prediction of COVID-19.

Unpretentiously exploring stock prices predictions problem by means of Deep Learning based models

This repo contains EDA(Exploratory Data Analysis) & Feature Engineering projects made with help of Python and their libraries.

Analyzing data from Liquor Sales In Iowa. From Data Cleaning to Data Visualization with Insights.

Program that can take in large amounts of .csv files in the same directory and trains a deep learning neural network model based on the best/worst students evaluated by the user. Second program then utilizes the model from the first program to produce a projected report for each individual student inputted.

Explore Titanic dataset to uncover survival insights. Analyze demographics, ticket class, and cabins. Understand factors influencing passenger survival.