imdb-top-100-movies-tv-shows-recommendation-sys.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2022-09-16T12:32:04.211792Z","iopub.status.busy":"2022-09-16T12:32:04.211344Z","iopub.status.idle":"2022-09-16T12:32:04.220041Z","shell.execute_reply":"2022-09-16T12:32:04.218853Z","shell.execute_reply.started":"2022-09-16T12:32:04.211758Z"},"trusted":true},"outputs":[],"source":["\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2022-09-16T12:32:04.223166Z","iopub.status.busy":"2022-09-16T12:32:04.222363Z","iopub.status.idle":"2022-09-16T12:32:04.244960Z","shell.execute_reply":"2022-09-16T12:32:04.244053Z","shell.execute_reply.started":"2022-09-16T12:32:04.223094Z"},"trusted":true},"outputs":[],"source":["cleaned = pd.read_csv(\"./imdb_top_1000.csv\")\n"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2022-09-16T12:32:04.247455Z","iopub.status.busy":"2022-09-16T12:32:04.246695Z","iopub.status.idle":"2022-09-16T12:32:04.278047Z","shell.execute_reply":"2022-09-16T12:32:04.277085Z","shell.execute_reply.started":"2022-09-16T12:32:04.247402Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.1\n","  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"]},{"data":{"text/plain":["(1000, 319)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["#Import TfIdfVectorizer from scikit-learn\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","#Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'\n","tfidf = TfidfVectorizer(stop_words='english',ngram_range=(1,3),min_df=3,analyzer='word')\n","\n","cleaned['overview'] = cleaned['Gross'].fillna('')\n","\n","#Construct the required TF-IDF matrix by fitting and transforming the data\n","tfidf_matrix = tfidf.fit_transform(cleaned['overview'])\n","\n","#Output the shape of tfidf_matrix\n","tfidf_matrix.shape"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2022-09-16T12:32:04.295208Z","iopub.status.busy":"2022-09-16T12:32:04.294606Z","iopub.status.idle":"2022-09-16T12:32:04.302638Z","shell.execute_reply":"2022-09-16T12:32:04.301534Z","shell.execute_reply.started":"2022-09-16T12:32:04.295175Z"},"trusted":true},"outputs":[],"source":["from sklearn.metrics.pairwise import linear_kernel\n","\n","# Compute the cosine similarity matrix\n","cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":[]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2022-09-16T12:32:04.305661Z","iopub.status.busy":"2022-09-16T12:32:04.304885Z","iopub.status.idle":"2022-09-16T12:32:04.312577Z","shell.execute_reply":"2022-09-16T12:32:04.311403Z","shell.execute_reply.started":"2022-09-16T12:32:04.305616Z"},"trusted":true},"outputs":[],"source":["#Construct a reverse map of indices and movie titles\n","indices = pd.Series(cleaned.index, index=cleaned['Series_Title']).drop_duplicates()"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2022-09-16T12:32:04.314551Z","iopub.status.busy":"2022-09-16T12:32:04.314095Z","iopub.status.idle":"2022-09-16T12:32:04.325530Z","shell.execute_reply":"2022-09-16T12:32:04.324299Z","shell.execute_reply.started":"2022-09-16T12:32:04.314509Z"},"trusted":true},"outputs":[],"source":["def get_recommendations(title, cosine_sim=cosine_sim):\n","    # Get the index of the movie that matches the title\n","    idx = indices[title]\n","\n","    # Get the pairwsie similarity scores of all movies with that movie\n","    sim_scores = list(enumerate(cosine_sim[idx]))\n","\n","    # Sort the movies based on the similarity scores\n","    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n","\n","    # Get the scores of the 10 most similar movies\n","    sim_scores = sim_scores[1:11]\n","\n","    # Get the movie indices\n","    movie_indices = [i[0] for i in sim_scores]\n","\n","    # Return the top 10 most similar movies\n","    return cleaned['Series_Title'].iloc[movie_indices]"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2022-09-16T12:32:04.328673Z","iopub.status.busy":"2022-09-16T12:32:04.327528Z","iopub.status.idle":"2022-09-16T12:32:04.345088Z","shell.execute_reply":"2022-09-16T12:32:04.343751Z","shell.execute_reply.started":"2022-09-16T12:32:04.328633Z"},"trusted":true},"outputs":[{"data":{"text/plain":["415                                   Jaws\n","447               A Streetcar Named Desire\n","466                         Marriage Story\n","484                           The Irishman\n","698    Willy Wonka & the Chocolate Factory\n","703                           My Fair Lady\n","566                              King Kong\n","183                       Some Like It Hot\n","850                       Enter the Dragon\n","987                       Midnight Express\n","Name: Series_Title, dtype: object"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["get_recommendations('Psycho')"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2022-09-16T12:32:04.347298Z","iopub.status.busy":"2022-09-16T12:32:04.346304Z","iopub.status.idle":"2022-09-16T12:32:04.357828Z","shell.execute_reply":"2022-09-16T12:32:04.356593Z","shell.execute_reply.started":"2022-09-16T12:32:04.347263Z"},"trusted":true},"outputs":[{"data":{"text/plain":["667              Night on Earth\n","595                Kaze tachinu\n","169              Dom za vesanje\n","591                 Vicky Donor\n","914             Sherlock Holmes\n","212               Hacksaw Ridge\n","469                Isle of Dogs\n","531           A Christmas Story\n","0      The Shawshank Redemption\n","1                 The Godfather\n","Name: Series_Title, dtype: object"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["get_recommendations('Coco')"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2022-09-16T12:32:04.430883Z","iopub.status.busy":"2022-09-16T12:32:04.430478Z","iopub.status.idle":"2022-09-16T12:32:04.440708Z","shell.execute_reply":"2022-09-16T12:32:04.439427Z","shell.execute_reply.started":"2022-09-16T12:32:04.430849Z"},"trusted":true},"outputs":[{"data":{"text/plain":["97          Requiem for a Dream\n","578    Kubo and the Two Strings\n","625                  Apocalypto\n","501               Les choristes\n","333                      Wonder\n","752     Silver Linings Playbook\n","37                 The Departed\n","631           Pride & Prejudice\n","951             Minority Report\n","0      The Shawshank Redemption\n","Name: Series_Title, dtype: object"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["get_recommendations('Close Encounters of the Third Kind')"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}