Regression analysis in machine learning is used to find the relationship between a dependent variable and one or more independent variables. The goal is to predict the value of dependent variable based on input features. In this article, we will explore different Machine learning Projects using Regression in 2025.

1. Stock Price Prediction

Stock market is a place where people buy and sell shares of publicly traded companies where investors aim to buy low and sell high. Machine Learning helps us analyzing past data to predict future stock prices. In this project we forecast prices using historical data. This helps investors make smarter decisions.

Stock Price Prediction using Machine Learning

2. Calories Burnt Prediction

This project uses ML to predict how many calories a person burns during an activity. It analyze data such as age, weight, heart rate, type of exercise and duration. It helps users to monitor their health and fitness goals better. Regression models like Linear Regression or Random Forest Regressor are trained using this data.

Calories Burnt Prediction

3. Vehicle Count Prediction

It helps us to predict traffic volume using data from traffic cameras, sensors and weather conditions. Computer vision techniques are used to count vehicles in videos. Results helps traffic management systems to improve city planning and reduce congestion.

Vehicle Count Prediction From Sensor Data

4. Box Office Revenue Prediction

This project predicts box office revenue by analyzing data like genre, cast, budget, release date and social media buzz. Sentiment analysis and historical performance helps to make smart predictions about a movie’s success. It helps studios choose better release dates and marketing strategies. We will be using XGBoost regressor for this.

Box Office Revenue Prediction

5. House Price Prediction

ML helps us to predict house prices by analyzing data on property features, neighborhood characteristics and economic factors. Models like Linear Regression and Random Forests learn from past data to spot patterns and forecast future prices. Geospatial analysis and sentiment from social media further enhance the predictions.

• House Price Prediction using Machine Learning

6. Medical Insurance Price Prediction

ML predicts medical insurance premiums by analyzing health profiles, demographics and lifestyle data. It identifies factors like age, conditions and family history to assess risk and set prices. Different Regression models are used.

Medical Insurance Price Prediction using Machine Learning

7. Inventory Demand Forecasting

It helps business to predict product demand by analyzing sales history and factors like promotions, seasonality and market trends. This helps optimize inventory, reduce costs and improve customer satisfaction

Inventory Demand Forecasting Using ML

8. Cab Ride Ride Request Forecast

It helps cab services predict ride demand more accurately. By analyzing historical data, including time, day, events and weather. It can forecast when and where rides will be needed. This helps manage driver availability, reduce wait times and improve service during busy hours.

Ola Bike Ride Request Forecast

9. Zillow Home Value Index Prediction

It improves Zillow's Home Value Index (ZHVI) by analyzing housing data, market trends and location. These models keep predictions up to date and accurate and provide users smart recommendations. It enhances user experiences through personalized recommendations and contributes to data quality by identifying and rectifying inaccuracies.

Zillow Home Value Prediction in ML

10. Ola Bike Ride Request Forecast using ML

It aims to predict the demand for Ola bikes rides using machine learning algorithms. This system use regression to predict future ride requests accurately. In this project it involves data preprocessing, feature engineering, model selection, training, validation and deployment.

Ola Bike Ride Request Forecast using ML

Regression based projects covered in this article give beginners a way to learn how to clean data, build predictive models and interpret results. These real-world examples not only boost your confidence but also add value to your portfolio.