A final project in Sharing Vision Data Science Bootcamp

Creating a Machine Learning model to predict the home prices.

In this project, the objective was to predict house prices in 6 metropolitan cities of India. The dataset provided contained essential features and amenities of houses in these cities. To achieve accurate predictions, a systematic approach was followed, encompassing exploratory data analysis, feature engineering and model building.

House Price Prediction using different regression models like Linear, Ridge, Lasso, Elastic Net, Random Forest, XGBoost, K-Nearest Neighbours, Support Vector Regressor, XGBoost. Also, multi-layer perceptron(MLP) was implemented using TensorFlow

Revolutionize sales forecasting for Rossmann stores with our high-accuracy XGBoost model, leveraging data analysis, feature engineering, and machine learning to predict sales up to six weeks in advance.

By applying data preprocessing, exploratory data analysis, feature selection, model training, and evaluation techniques, develop a predictive model that can accurately predict the survival status of passengers aboard the Titanic.

Kaggle competition

Predicting House Sales Prices Using Advanced Regression Techniques

Predict if a review is food relevant of irrelevant

Apply unsupervised learning techniques to identify segments of a customer base

Detailed Exploratory Data Analysis along with feature engineering to predict price of cars using several Sklearn Models.

project from web scrapping an ecommerce website product reviews (user required) till deployment of the model to local machine can be stored using MongoDb And deployment of same on cloud platform like Heroku, Azure.

Machine learning models for making predictions of titanic survivors

Text Classification

The fameous Titanic project on Kaggle.com

Project for Machine Learning Engineer Nanodegree, unit 4 (Machine Learning, Case Studies).

Project 5 OPENCLASSROOMS: Customer Segmentation