Machine Learning Projects

with Source Code

House Price Prediction

In this project, you can learn to predict the prices of houses by using the dataset with the XGBoost and the Linear regression algorithm. The dataset contains information about the house’s location, the house’s price, the house, square feet of the house, and more.

Music Recommendation System

One of the most famous music apps, Spotify, always shows music that you may like. This app works by using machine learning algorithms. In this project, youwill initially predict the possibility of the user listening to a song on a loop within a time frame. In this project, you can generate a content-based music recommendation system that uses a dataset of names, artists, and lyrics of 57650 songs in English obtained from Kaggle.

Loan Prediction using Machine Learning

With this project, you can build a machine-learning model that will help to analyze how much loan the user can take. Different machine learning models like logistic regression, decision trees, random forest, and XGBoost are implemented in this project. The dataset used in this project consists of user information such as marital status, education, number of dependents, employment, and more.

Iris Flowers Classification ML Project

The Iris flower dataset is the most widely used dataset for classification. It consists of three different flowers, such as Setosa, Virginia, and Versicolor, and each flower contains features like Sepal width, Sepal length, Petal length, and Petal width. In this project, you can learn to train the Support vector machine and supervised machine learning models with the iris flower dataset.

Wine Quality Prediction

The wine quality prediction project is to build a machine-learning model to detect the quality of wines by using different chemical properties in the wine dataset. In this project, each wine in the dataset is given a quality score between 0 to 10. As a result, the output of this project is calculated as good when the score of wine is seven or greater than seven. The output is treated as bad-quality wine when the score is below seven.

Market Basket Analysis

In this project, you learn to conduct a Market Basket analysis to predict consumer purchasing behaviors. The output of this project is split into three sections. The first section is to source, explore, and format a complex dataset suitable for modeling with recommendation algorithms. In the second part, you can apply various machine-learning algorithms for product recommendation and select the best-performing model with the help of the ‘recommenderlab’ package.

Text Summarization

In this project, the text summarization is performed while conserving its meaning. In this project, extractive text summarization employs a scoring function for recognizing and picking essential pieces of text from documents and compiling them into an edited version of the original text.

Black Friday Sales Prediction

Black Friday is celebrated on Friday following Thanksgiving Day in the United States on the 4th Thursday of November. This project determines the product price with the help of historical retail store sales data. The dataset in this project is taken from the online analytics hackathon hosted by Analytics Vidhya. It consists of attributes such as marital status, gender, product categories, purchase amount, and city demographics.

BigMart Sales Prediction ML Project

This project will help you with different unsupervised machine learning algorithms by using the sales dataset of a grocery supermarket store. BigMart sales dataset contains sales data for 2013 for 1559 products across ten different outlets in different cities. This project aims to build a regression model to predict sales of 1559 products for the following year in each of the ten different BigMart outlets.

Sales Forecasting using the Walmart dataset

Sales forecasting is one of the most common use cases of machine learning to identify factors that affect product sales and estimate future sales volume. This project uses the Walmart dataset of sales data for 98 products across 45 outlets. The dataset contains sales per store and department weekly.



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