IMDB-Movie-Ratings-Prediction-with-Machine-Learning

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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.

Here are the typical steps taken in this machine learning project for predicting IMDB ratings:

1. Defined the Problem:

We clearly defined the problem we want to solve. In this case, it's predicting IMDB ratings.

2. Gathered Data:

we collected relevant data for this project. This could include information about movies, director name, duration and other features that might influence IMDB ratings.

3. Explored and Understood the Data:

 Performed exploratory data analysis (EDA) to understand the characteristics of the dataset. Checked for missing values, outliers, and patterns in the data.

4. Data Visualization:

 Used seaborn and matplotlib for visualization of the data.

5. Preprocessed 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.

6. Feature Engineering:

 Created new features to improve the performance of the machine learning model.

7. Splitted the Data:

 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.

8. Selected a Model:

 Choosed a machine learning algorithm suitable for regression tasks. Common choices include linear regression, decision trees, random forests, or gradient boosting algorithms.

9. Trained the Model:

 Use the training data to train the selected model. The model learns the patterns in the data to make predictions.

10. Tune Hyperparameters:

Optimized the model by tuning hyperparameters. This involves adjusting parameters that are not learned during training to improve performance.

11. Evaluate the Model:

  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.

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imdb-movie-ratings-prediction-with-machine-learning's Introduction