Market_Basket_Analysis_Using_Apriori_Algo.ipynb

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      "\n",
      "User Data :\n",
      "shape :  (943, 5)\n",
      "\n",
      "Ratings Data :\n",
      "shape :  (100000, 4)\n",
      "\n",
      "Item Data :\n",
      "shape :  (1682, 24)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from apyori import apriori\n",
    "\n",
    "# reading users file:\n",
    "u_cols = ['user_id', 'age', 'sex', 'occupation', 'zip_code']\n",
    "users = pd.read_csv('u.user', sep='|', names=u_cols,encoding='latin-1')\n",
    "\n",
    "# reading ratings file:\n",
    "r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n",
    "ratings = pd.read_csv('u.data', sep='\\t', names=r_cols,encoding='latin-1')\n",
    "\n",
    "# reading items file:\n",
    "i_cols = ['movie id', 'movie title' ,'release date','video release date', 'IMDb URL', 'unknown', 'Action', 'Adventure',\n",
    "'Animation', 'Children\\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',\n",
    "'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']\n",
    "items = pd.read_csv('u.item', sep='|', names=i_cols,\n",
    "encoding='latin-1')\n",
    "\n",
    "# After loading the dataset, we should look at the content of each file (users, ratings, items).\n",
    "\n",
    "# Looking at the user file\n",
    "print(\"\\nUser Data :\")\n",
    "print(\"shape : \", users.shape)\n",
    "#print(users.head())\n",
    "\n",
    "# We have 943 users in the dataset and each user has 5 features, i.e. user_ID, age, sex, occupation and zip_code. Now let’s look at the ratings file.\n",
    "\n",
    "# Ratings Data\n",
    "print(\"\\nRatings Data :\")\n",
    "print(\"shape : \", ratings.shape)\n",
    "#print(ratings.head())\n",
    "\n",
    "# We have 100k ratings for different user and movie combinations. Now finally examine the items file.\n",
    "\n",
    "# Item Data\n",
    "print(\"\\nItem Data :\")\n",
    "print(\"shape : \", items.shape)\n",
    "#print(items.head())"
   ]
  },
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       "   user_id  age sex  occupation zip_code\n",
       "0        1   24   M  technician    85711\n",
       "1        2   53   F       other    94043\n",
       "2        3   23   M      writer    32067\n",
       "3        4   24   M  technician    43537\n",
       "4        5   33   F       other    15213"
      ]
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    "users.head()"
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       "   user_id  movie_id  rating  unix_timestamp\n",
       "0      196       242       3       881250949\n",
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       "2       22       377       1       878887116\n",
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    "ratings.head()"
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       "      <th>video release date</th>\n",
       "      <th>IMDb URL</th>\n",
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       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
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       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   movie id        movie title release date  ...  Thriller War  Western\n",
       "0         1   Toy Story (1995)  01-Jan-1995  ...         0   0        0\n",
       "1         2   GoldenEye (1995)  01-Jan-1995  ...         1   0        0\n",
       "2         3  Four Rooms (1995)  01-Jan-1995  ...         1   0        0\n",
       "3         4  Get Shorty (1995)  01-Jan-1995  ...         0   0        0\n",
       "4         5     Copycat (1995)  01-Jan-1995  ...         1   0        0\n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {
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   "source": [
    "items.head()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# **Apriori Algorithm**"
   ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
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       "      <td>False</td>\n",
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       "      <td>891035994</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>224</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
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       "      <td>False</td>\n",
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      "text/plain": [
       "    user_id  movie_id  rating  unix_timestamp  favorable_rev\n",
       "10       62       257       2       879372434          False\n",
       "11      286      1014       5       879781125           True\n",
       "12      200       222       5       876042340           True\n",
       "13      210        40       3       891035994          False\n",
       "14      224        29       3       888104457          False"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
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    }
   ],
   "source": [
    "# I just want to deal with favorable reviews in my dataset\n",
    "ratings[\"favorable_rev\"] =ratings[\"rating\"] > 3\n",
    "ratings[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "943"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
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   ],
   "source": [
    "#Total number of unique users\n",
    "len(ratings['user_id'].unique())"
   ]
  },
  {
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    },
    "id": "T-1md6z2Uf3m",
    "outputId": "65ff8362-f681-44f0-c647-d81f0c6ac6b9"
   },
   "outputs": [],
   "source": [
    "# The dataset has been sampled because of the longer running time hence we would deal with 220 users with preferrable ratings\n",
    "ratings = ratings[ratings['user_id'].isin(range(220))] \n",
    "# We start by creating a dataset of each user's favourable reviews\n",
    "favorable_ratings = ratings[ratings[\"favorable_rev\"]]\n",
    "favorable_ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DGckA9QIUf7c",
    "outputId": "e2bc3e55-831a-4b5e-91e5-7da28e79bea0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the users having more than one reviews\n",
    "favorable_reviews_by_users = dict((k, v.values) for k, v in favorable_ratings.groupby(\"user_id\")[\"movie_id\"])\n",
    "len(favorable_reviews_by_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NnSajalpwWoI",
    "outputId": "c6aed85b-dab0-481a-b849-f53670750d99"
   },
   "outputs": [],
   "source": [
    "#favorable_reviews_by_users depicts the dictionary having key as user_id and values as the movie_ids they have highly rated\n",
    "favorable_reviews_by_users[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "id": "7TyIiS-aiNkT",
    "outputId": "f47d9e96-49c0-4271-97f7-d458152cacd8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>favorable_rev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          favorable_rev\n",
       "movie_id               \n",
       "50                  108\n",
       "100                  94\n",
       "258                  89\n",
       "181                  84\n",
       "174                  81"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out how many movies have favourable ratingsthe descending order by which movies have got the number of favorable ratings\n",
    "favorable_ratings_by_movie = ratings[[\"movie_id\", \"favorable_rev\"]].groupby(\"movie_id\").sum()\n",
    "favorable_ratings_by_movie.sort_values(by=[\"favorable_rev\"], ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "Ak_KqAV0Axma",
    "outputId": "d997b5d2-c3b7-48ef-e582-2c1639413c34"
   },
   "outputs": [],
   "source": [
    "#Here's a look at starting movieids and the number of received favorable ratings\n",
    "favorable_ratings_by_movie.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "Dl3C1QsriNdR"
   },
   "outputs": [],
   "source": [
    "frequent_itemsets = {}\n",
    "min_support = 40\n",
    "frequent_itemsets[1] = dict((frozenset((movie_id,)), row[\"favorable_rev\"]) for movie_id, row in favorable_ratings_by_movie.iterrows() if row[\"favorable_rev\"] > min_support)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TFfvSeRc5spu",
    "outputId": "ee24a3ee-0493-4218-bf4f-b13198209484"
   },
   "outputs": [],
   "source": [
    "#Now frequent_itemsets[1] depcits a dictionary having movieids as a key and number of favorable reviews as values\n",
    "frequent_itemsets[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "CnGXecj9iNWp"
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "def find_frequent_itemsets(favorable_reviews_by_users, k_1_itemsets, min_support): #k_1 implies k-1\n",
    "    counts = defaultdict(int)\n",
    "    for user, reviews in favorable_reviews_by_users.items():\n",
    "      #itemset contains only those movies which have been given favorable ratings above a threshold min_support value\n",
    "        for itemset in k_1_itemsets:\n",
    "            if itemset.issubset(reviews):\n",
    "                for other_reviewed_movie in set(reviews) - itemset:\n",
    "                    #Now the other reviewed movie and the itemset movie has been watched by user, so increase its count by 1\n",
    "                    current_superset = itemset | frozenset((other_reviewed_movie,))\n",
    "                    counts[current_superset] += 1\n",
    "                    #print('counts=',counts)\n",
    "                    #print('current_superset=',current_superset)\n",
    "    #Now frequency greater than min_support is taken as a condition because obviously we desire that the confidence of watching b given \n",
    "    # a should be atleast greater than a threshold   \n",
    "    #print(counts)             \n",
    "    return dict([(itemset, frequency) for itemset, frequency in counts.items() if frequency >= min_support])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hNc6PAS5iNQx",
    "outputId": "e02ca1ca-f74b-4f19-a3b0-e1111dad3ccb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 15 movies with more than 60 favorable reviews\n",
      "82 frequent itemsets of length 2 have been found\n",
      "246 frequent itemsets of length 3 have been found\n",
      "450 frequent itemsets of length 4 have been found\n",
      "517 frequent itemsets of length 5 have been found\n",
      "374 frequent itemsets of length 6 have been found\n",
      "165 frequent itemsets of length 7 have been found\n",
      "40 frequent itemsets of length 8 have been found\n",
      "4 frequent itemsets of length 9 have been found\n",
      "Did not find any frequent itemsets of length 10\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "frequent_itemsets = {}  # itemsets are sorted by length\n",
    "min_support = 60\n",
    "\n",
    "# k=1 candidates are the movies with more than min_support favourable reviews\n",
    "frequent_itemsets[1] = dict((frozenset((movie_id,)), row[\"favorable_rev\"])\n",
    "                                for movie_id, row in favorable_ratings_by_movie.iterrows()\n",
    "                                if row[\"favorable_rev\"] > min_support)\n",
    "\n",
    "print(\"There are {} movies with more than {} favorable reviews\".format(len(frequent_itemsets[1]), min_support))\n",
    "sys.stdout.flush()\n",
    "for k in range(2, 20):\n",
    "    # Generate candidates of length k, using the frequent itemsets of length k-1\n",
    "    # Only store the frequent itemsets having a frequency greater than min_support\n",
    "    cur_frequent_itemsets = find_frequent_itemsets(favorable_reviews_by_users, frequent_itemsets[k-1],min_support)\n",
    "    if len(cur_frequent_itemsets) == 0:\n",
    "        print(\"Did not find any frequent itemsets of length {}\".format(k))\n",
    "        sys.stdout.flush()\n",
    "        break\n",
    "    else:\n",
    "        print(\"{} frequent itemsets of length {} have been found\".format(len(cur_frequent_itemsets), k))\n",
    "        sys.stdout.flush()\n",
    "        frequent_itemsets[k] = cur_frequent_itemsets        \n",
    "# Obviously in market basket analysis, it doesn't make sense to take consideration of unique movies as we desire a set of atleast two watched movies.\n",
    "del frequent_itemsets[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "opVo94bj9ul_",
    "outputId": "75e052a5-7495-4763-c0a2-24829e474547"
   },
   "outputs": [],
   "source": [
    "#The below line will display the movie ids in keys along with their frequencies as values and its gonna increase from 1 movie to 2 movies which are \n",
    "#highly watched/rated together \n",
    "print(frequent_itemsets) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wYUnYNu3NIKe",
    "outputId": "c33fcfc1-3e9b-4359-e96b-953c96bbbc76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{frozenset({7, 50, 56, 64, 79, 98, 100, 172, 174}): 117,\n",
       " frozenset({7, 50, 56, 64, 79, 98, 172, 174, 181}): 135,\n",
       " frozenset({7, 50, 56, 64, 98, 100, 172, 174, 181}): 108,\n",
       " frozenset({7, 50, 56, 79, 98, 172, 174, 181, 258}): 126}"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ltno85j6LSwQ",
    "outputId": "06d7e0e1-f5fa-4df2-aaf8-8d13b8610d48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[['Shawshank Redemption, The (1994)'],\n",
       "        ['Silence of the Lambs, The (1991)'],\n",
       "        ['Fargo (1996)'],\n",
       "        ['Twelve Monkeys (1995)'],\n",
       "        ['Empire Strikes Back, The (1980)'],\n",
       "        ['Raiders of the Lost Ark (1981)'],\n",
       "        ['Fugitive, The (1993)'],\n",
       "        ['Star Wars (1977)'],\n",
       "        ['Pulp Fiction (1994)']],\n",
       "\n",
       "       [['Shawshank Redemption, The (1994)'],\n",
       "        ['Silence of the Lambs, The (1991)'],\n",
       "        ['Twelve Monkeys (1995)'],\n",
       "        ['Empire Strikes Back, The (1980)'],\n",
       "        ['Raiders of the Lost Ark (1981)'],\n",
       "        ['Fugitive, The (1993)'],\n",
       "        ['Star Wars (1977)'],\n",
       "        ['Return of the Jedi (1983)'],\n",
       "        ['Pulp Fiction (1994)']],\n",
       "\n",
       "       [['Shawshank Redemption, The (1994)'],\n",
       "        ['Silence of the Lambs, The (1991)'],\n",
       "        ['Fargo (1996)'],\n",
       "        ['Twelve Monkeys (1995)'],\n",
       "        ['Empire Strikes Back, The (1980)'],\n",
       "        ['Raiders of the Lost Ark (1981)'],\n",
       "        ['Star Wars (1977)'],\n",
       "        ['Return of the Jedi (1983)'],\n",
       "        ['Pulp Fiction (1994)']],\n",
       "\n",
       "       [['Silence of the Lambs, The (1991)'],\n",
       "        ['Contact (1997)'],\n",
       "        ['Twelve Monkeys (1995)'],\n",
       "        ['Empire Strikes Back, The (1980)'],\n",
       "        ['Raiders of the Lost Ark (1981)'],\n",
       "        ['Fugitive, The (1993)'],\n",
       "        ['Star Wars (1977)'],\n",
       "        ['Return of the Jedi (1983)'],\n",
       "        ['Pulp Fiction (1994)']]], dtype='<U32')"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for lets look at the movies in frequent_itemsets[9]. These have the hisghest chances of occuring together \n",
    "u=[]\n",
    "for itemset in frequent_itemsets[9]:\n",
    "  v=[]\n",
    "  for k in set(itemset):\n",
    "    v.append(list(items[items['movie id']==k]['movie title']))\n",
    "  u.append(v) \n",
    "u=np.array(u)  \n",
    "u  \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gsd2bebmLS2t"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Market Basket Analysis Using Apriori Algo.ipynb",
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