CODSOFT

DataScience tasks for Codsoft Internship:

• Movie Rating Prediction: Build a model that predicts the rating of a movie based on features like genre, director, and actors. You can use regression techniques to tackle this problem. The goal is to analyze historical movie data and develop a model that accurately estimates the rating given to a movie by users or critics. Movie Rating Prediction project enables you to explore data analysis, preprocessing, feature engineering, and machine learning modeling techniques. It provides insights into the factors that influence movie ratings and allows you to build a model that can estimate the ratings of movies accurately.

• Sales Prediction Using Regression: Sales prediction involves forecasting the amount of a product that customers will purchase, taking into account various factors such as advertising expenditure, target audience segmentation, and advertising platform selection. In businesses that offer products or services, the role of a Data Scientist is crucial for predicting future sales. They utilize machine learning techniques in Python to analyze and interpret data, allowing them to make informed decisions regarding advertising costs. By leveraging these predictions, businesses can optimize their advertising strategies and maximize sales potential.

• Credit Card Fraud Detection: Building a machine learning model to identify fraudulent credit card transactions. Preprocess and normalize the transaction data, handle class imbalance issues, and split the dataset into training and testing sets. Train a classification algorithm, such as logistic regression or random forests, to classify transactions as fraudulent or genuine. Evaluate the model's performance using metrics like precision, recall, and F1-score, and consider techniques like oversampling or undersampling for improving results. The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.