The project investigates the effectiveness of different machine learning models in predicting sales. XGBoost Regression with tuned hyperparameters achieved the best performance among the tested models based on the Mean Squared Error metric.

The IPL EDA (Exploratory Data Analysis) was conducted, revealing valuable insights. The analysis focused on various aspects such as player performance, team statistics, and match outcomes. Key findings include trends in run-scoring, top performers, and team dynamics. The EDA offers actionable insights for teams and fans to make data-driven decision

This project explores the optimal combination of Bag-of-Words and TF-IDF vectorization with Naive Bayes and SVM for sentiment analysis. It evaluates performance using accuracy, precision, recall, and F1-score, addressing ethical concerns like data privacy and bias to improve sentiment classification in real-world applications.

In this project a convolutional neural network (CNN) is trained to learn the behavior of a car using data from a simulator that allows real-time information gathering from the car’s chassis, position and its speed. As a first step the vehicle is driven in a manual mode of simulation for collecting data. Then the neural network uses information f…