In this project, we are going to use machine learning models to predict IMDB ratings of any particular movie. This is going to be a regression problem.
We clearly defined the problem we want to solve. In this case, it's predicting IMDB ratings.
we collected relevant data for this project. This could include information about movies, director name, duration and other features that might influence IMDB ratings.
Performed exploratory data analysis (EDA) to understand the characteristics of the dataset. Checked for missing values, outliers, and patterns in the data.
Used seaborn and matplotlib for visualization of the data.
Handled missing values, encode categorical variables, scale numerical features, and perform any necessary data transformations to prepare the data for machine learning models.
Created new features to improve the performance of the machine learning model.
Divided the dataset into training and testing sets. The training set is used to train the model, and the testing set is used to evaluate its performance.
Choosed a machine learning algorithm suitable for regression tasks. Common choices include linear regression, decision trees, random forests, or gradient boosting algorithms.
Use the training data to train the selected model. The model learns the patterns in the data to make predictions.
Optimized the model by tuning hyperparameters. This involves adjusting parameters that are not learned during training to improve performance.
Use the testing set to evaluate the model's performance. Common evaluation metrics for regression tasks include Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared.