Movie Recommender.ipynb

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   "source": [
    "## Movie Recommender using KNN algorithm"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "| 12 Years a Slave\n",
      "| Hacksaw Ridge\n",
      "| Queen of Katwe\n",
      "| The Wind Rises\n",
      "| A Beautiful Mind\n"
     ]
    }
   ],
   "source": [
    "from knn_algorithm import knn, euclideanDistance\n",
    "\n",
    "def recommend_movies(movie_query, k):\n",
    "    raw_data = []\n",
    "    with open('movies_recommendation.csv', 'r') as movies_data:\n",
    "        # Discard the first line(headings)\n",
    "        next(movies_data)\n",
    "\n",
    "        # Read the data into memory\n",
    "        for line in movies_data.readlines():\n",
    "            data_row = line.strip().split(',')\n",
    "            raw_data.append(data_row)\n",
    "            \n",
    "            \n",
    "    # Pick the relevant columns and convert the numeric columns to numbers since they were read in as strings\n",
    "    movies_recommendation_data = []\n",
    "    \n",
    "    for row in raw_data:\n",
    "        data_row = list(map(float, row[2:]))\n",
    "        movies_recommendation_data.append(data_row)\n",
    "        \n",
    "    # Use the KNN algorithm to get the 5 movies that are most similar to the movie query.\n",
    "    recommendation_indices = knn(movies_recommendation_data, movie_query, k)\n",
    "   \n",
    "    movie_recommendations = []\n",
    "    \n",
    "    for distance, index in recommendation_indices:\n",
    "        movie_recommendations.append(raw_data[index])\n",
    "\n",
    "    return movie_recommendations\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    the_post = [7.2, 1, 1, 0, 0, 0, 0, 1, 0] # feature vector for The Post\n",
    "    recommended_movies = recommend_movies(the_post, 5)\n",
    "\n",
    "    # Print recommended movie titles\n",
    "    for recommendation in recommended_movies:\n",
    "        print(\"| \"+ recommendation[1])"
   ]
  }
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