From 6909c57c36000cab417b031dfce9b206d3c8554d Mon Sep 17 00:00:00 2001 From: saikumar934 Date: Fri, 25 Oct 2024 21:42:41 +0530 Subject: [PATCH] Update Project_Outline.ipynb --- Project_Outline.ipynb | 193 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 192 insertions(+), 1 deletion(-) diff --git a/Project_Outline.ipynb b/Project_Outline.ipynb index e47f144..8f23bab 100644 --- a/Project_Outline.ipynb +++ b/Project_Outline.ipynb @@ -1 +1,192 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Project Outline.ipynb","provenance":[],"authorship_tag":"ABX9TyPZl4d0nA5Qmq8X1mDqSb1O"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Title of Project**"],"metadata":{"id":"dqZ-nhxiganh"}},{"cell_type":"markdown","source":["-------------"],"metadata":{"id":"gScHkw6jjrLo"}},{"cell_type":"markdown","source":["## **Objective**"],"metadata":{"id":"Xns_rCdhh-vZ"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"9sPvnFM1iI9l"}},{"cell_type":"markdown","source":["## **Data Source**"],"metadata":{"id":"-Vbnt9CciKJP"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"sGcv5WqQiNyl"}},{"cell_type":"markdown","source":["## **Import Library**"],"metadata":{"id":"r7GrZzX0iTlV"}},{"cell_type":"code","source":[""],"metadata":{"id":"UkK6NH9DiW-X"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Import Data**"],"metadata":{"id":"9lHPQj1XiOUc"}},{"cell_type":"code","source":[""],"metadata":{"id":"zcU1fdnGho6M"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Describe Data**"],"metadata":{"id":"7PUnimBoiX-x"}},{"cell_type":"code","source":[""],"metadata":{"id":"kG15arusiZ8Z"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Data Visualization**"],"metadata":{"id":"oBGX4Ekniriz"}},{"cell_type":"code","source":[""],"metadata":{"id":"lW-OIRK0iuzO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Data Preprocessing**"],"metadata":{"id":"UqfyPOCYiiww"}},{"cell_type":"code","source":[""],"metadata":{"id":"3cyr3fbGin0A"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Define Target Variable (y) and Feature Variables (X)**"],"metadata":{"id":"2jXJpdAuiwYW"}},{"cell_type":"code","source":[""],"metadata":{"id":"QBCakTuli57t"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Train Test Split**"],"metadata":{"id":"90_0q_Pbi658"}},{"cell_type":"code","source":[""],"metadata":{"id":"u60YYaOFi-Dw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Modeling**"],"metadata":{"id":"cIhyseNria7W"}},{"cell_type":"code","source":[""],"metadata":{"id":"Toq58wpkjCw7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Model Evaluation**"],"metadata":{"id":"vhAwWfG0jFun"}},{"cell_type":"code","source":[""],"metadata":{"id":"lND3jJj_jhx4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Prediction**"],"metadata":{"id":"8AzwG7oLjiQI"}},{"cell_type":"code","source":[""],"metadata":{"id":"JLebGzDJjknA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## **Explaination**"],"metadata":{"id":"SBo38CJZjlEX"}},{"cell_type":"markdown","source":[""],"metadata":{"id":"Ybi8FR9Kjv00"}}]} \ No newline at end of file +TITLE : movie recommendation system + +Libraries: + +1. Pandas for data manipulation +2. NumPy for numerical computations +3. Scikit-learn for machine learning +4. Matplotlib and Seaborn for visualization + +Dataset: + +We'll use the MovieLens dataset, which contains: + +1. 100,000 ratings from 600 users on 9,000 movies +2. Movie genres, titles, and release years + +Project Structure: + +1. Data Preprocessing + + +import pandas as pd + +# Load ratings and movies datasets +ratings = pd.read_csv('ratings.csv') +movies = pd.read_csv('movies.csv') + +# Merge datasets on movie ID +data = pd.merge(ratings, movies, on='movieId') + + +2. Exploratory Data Analysis + + +import matplotlib.pyplot as plt +import seaborn as sns + +# Visualize rating distribution +sns.histplot(data['rating']) +plt.show() + +# Top 10 most rated movies +top_movies = data['movieId'].value_counts().head(10) +print(top_movies) + + +3. Recommendation Algorithm + +We'll implement a simple collaborative filtering algorithm using Matrix Factorization. + + +from sklearn.decomposition import NMF + +# Create user-item matrix +user_item_matrix = pd.pivot_table(data, values='rating', index='userId', columns='movieId') + +# Apply Matrix Factorization +nmf = NMF(n_components=10) +user_features = nmf.fit_transform(user_item_matrix) +movie_features = nmf.components_.T + + +4. Recommendation Generation + + +def recommend_movies(user_id, num_recommendations): + # Get user features + user_feature = user_features[user_id] + + # Calculate similarities with all movies + similarities = cosine_similarity(user_feature.reshape(1, -1), movie_features) + + # Get top N recommended movies + recommended_movies = movies.iloc[similarities.argsort()[-num_recommendations:]] + + return recommended_movies + +# Test recommendation +recommended_movies = recommend_movies(1, 5) +print(recommended_movies) + + +5. Evaluation + + +from sklearn.metrics import precision_score, recall_score + +# Evaluate recommendation accuracy +precision = precision_score(data['rating'], recommended_movies['rating']) +recall = recall_score(data['rating'], recommended_movies['rating']) + +print(f'Precision: {precision:.3f}, Recall: {recall:.3f}') + + +Example Use Cases: + +1. User-based recommendations: Recommend movies to users based on their past ratings. +2. Movie-based recommendations: Recommend similar movies to users who liked a particular movie. +3. Hybrid recommendations: Combine user-based and movie-based recommendations. + +Future Enhancements: + +1. Incorporate additional data (e.g., genres, directors, actors) +2. Implement more advanced algorithms (e.g., Deep Learning, Graph-based methods) +3. Develop a web application for interactive recommendations +Here's the complete code for the movie recommendation system: + + +# Import necessary libraries +import pandas as pd +import numpy as np +from sklearn.decomposition import NMF +from sklearn.metrics.pairwise import cosine_similarity +from sklearn.metrics import precision_score, recall_score +import matplotlib.pyplot as plt +import seaborn as sns + +# Load MovieLens dataset +ratings = pd.read_csv('ratings.csv') +movies = pd.read_csv('movies.csv') + +# Merge datasets on movie ID +data = pd.merge(ratings, movies, on='movieId') + +# Exploratory Data Analysis +sns.histplot(data['rating']) +plt.title('Rating Distribution') +plt.show() + +# Top 10 most rated movies +top_movies = data['movieId'].value_counts().head(10) +print('Top 10 Most Rated Movies:') +print(top_movies) + +# Create user-item matrix +user_item_matrix = pd.pivot_table(data, values='rating', index='userId', columns='movieId') + +# Apply Matrix Factorization +nmf = NMF(n_components=10) +user_features = nmf.fit_transform(user_item_matrix) +movie_features = nmf.components_.T + +# Recommendation function +def recommend_movies(user_id, num_recommendations): + # Get user features + user_feature = user_features[user_id] + + # Calculate similarities with all movies + similarities = cosine_similarity(user_feature.reshape(1, -1), movie_features) + + # Get top N recommended movies + recommended_movies = movies.iloc[similarities.argsort()[-num_recommendations:]] + + return recommended_movies + +# Test recommendation +recommended_movies = recommend_movies(1, 5) +print('Recommended Movies for User 1:') +print(recommended_movies) + +# Evaluate recommendation accuracy +precision = precision_score(data['rating'], recommended_movies['rating']) +recall = recall_score(data['rating'], recommended_movies['rating']) + +print(f'Precision: {precision:.3f}, Recall: {recall:.3f}') + + +Dataset: + +You can download the MovieLens dataset from: + +(link unavailable) + +Note: + +- This code uses the ml-latest-small dataset, which contains 100,000 ratings. +- For larger datasets, consider using more advanced techniques or optimized libraries. + +Requirements: + +- Python 3.x +- Pandas +- NumPy +- Scikit-learn +- Matplotlib +- Seaborn + +Future Enhancements: + +1. Incorporate additional data (e.g., genres, directors, actors) +2. Implement more advanced algorithms (e.g., Deep Learning, Graph-based methods) +3. Develop a web application for interactive recommendations