Gitanjali Khatri 's repositories
Census-Income-Prediction-Project
This project consists of data visualization tools like matplotlib, seaborn, etc ., used to provide the basic understanding of the data . After data cleaning , some machine learning algorithms has been used to get the desired results..
Diwali-Sales-Analysis
This project is basically provides key insights and basic checks on data visualisation, done with the help of sample dataset in csv format.
House-Price-Prediction
This project consists visualisation and anlysis techniques such as ml models, feature engineering ,etc.
Telecom-Churn-Analysis
This project contains data on telecom industry regarding churn of customers . To identify and analyse the rate at which customers are switching to other companies, the analysis is done with eda, data visualisation and feature scaling and various machine learning algorithms.
Titanic-Survival-Prediction
This repository hosts a machine learning project aimed at predicting the survival of passengers on the Titanic. The project utilizes historical data such as passenger class, age, sex, and fare to train a variety of classification models. The goal is to accurately predict whether a passenger would have survived the Titanic disaster.
Uber_DSG
Uber Demand Supply Gap Prediction. This repository contains a data-driven project that aims to predict the demand and supply of Uber rides in a given area based on historical data. The project uses machine learning algorithms and time-series analysis to forecast the number of Uber rides requested and fulfilled.
Abalone-Age-Prediction
The dataset used in this project is from the UCI Machine Learning Repository. It includes measurements such as length, diameter, height, whole weight, shucked weight, viscera weight, shell weight, and gender.
Calories-burnt-prediction
This repository contains the code for a machine learning project that predicts the number of calories burnt based on various factors such as age, weight, height, gender, and physical activity level. The project uses a variety of regression models and data preprocessing techniques to make accurate predictions.
Iris-flower-classification
This project is about classifying iris flowers into one of three species: Setosa, Versicolour, and Virginica. The classification is based on the length and width of the sepals and petals of the flowers.
Wine-Quality-Prediction-Model
This repository contains the code and related files for a machine learning model that predicts the quality of wine based on various physicochemical properties.