This project aims to predict the success of movies using machine learning algorithms. It involves data preprocessing, exploratory data analysis (EDA), feature selection, and model training using various classification algorithms.
The dataset used in this project contains information about movies, including features such as budget, runtime, release year, and more. You can find the datasets in the datasets.zip file.
EDA involves analyzing the dataset to understand its characteristics and relationships between variables. Various visualizations and statistical analyses are performed to gain insights into the data.
Features are selected based on intuition and, in another approach, using Principal Component Analysis (PCA) to reduce dimensionality and improve model performance.
Several classification algorithms are trained and evaluated for predicting movie success, including:
- Logistic Regression
- Decision Tree
- Random Forest
- XGBoost
The performance of each model is evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC curves.
A simple Plot description-based Recommender is implemented based on the overview. It uses TF-IDF.
To use this project, follow these steps:
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Clone the repository:
git clone https://github.com/Arshiagosh/TMDB-5000-Movie-Dataset-Analysis-and-Modeling
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Install the required dependencies:
pip install -r requirements.txt
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Extract the datasets from datasets.zip.
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Run the Jupyter notebook or Python scripts to explore the data, train models, and make predictions.