recommender_homework_baysinger.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
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   "source": [
    "#download the class movie reviews from here:\n",
    "#https://docs.google.com/spreadsheets/d/17rCJzmWxqvAu9rkpkgt4ToccIlY4A1Ffuu1W9X3B8Ag/\n",
    "#then read that in as a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "M = pd.read_csv(\"Movie Reviews.csv\", index_col='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "def pearson(s1, s2):\n",
    "    \"\"\"Take two pd.Series objects and return a pearson correlation.\"\"\"\n",
    "    s1_c = s1 - s1.mean()\n",
    "    s2_c = s2 - s2.mean()\n",
    "    return np.sum(s1_c * s2_c) / np.sqrt(np.sum(s1_c ** 2) * np.sum(s2_c ** 2))"
   ]
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   "cell_type": "code",
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   "source": [
    "def get_recs(movie_name, M, num):\n",
    "\n",
    "    import numpy as np\n",
    "    reviews = []\n",
    "    for title in M.columns:\n",
    "        if title == movie_name:\n",
    "            continue\n",
    "        cor = pearson(M[movie_name], M[title])\n",
    "        if np.isnan(cor):\n",
    "            continue\n",
    "        else:\n",
    "            reviews.append((title, cor))\n",
    "    \n",
    "    reviews.sort(key=lambda tup: tup[1], reverse=True)\n",
    "    return reviews[:num]\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Question 1:  What movie is most Similar to 'The fault in our stars' (60 pts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [],
   "source": [
    "recs = get_recs('The Fault in Our Stars', M, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Malificent', 0.39873118100855826),\n",
       " ('Godzilla', 0.27910171761590286),\n",
       " ('How to Train your Dragon 2', 0.22514201090703723),\n",
       " ('Gone Girl', 0.14705909007675133),\n",
       " ('Interstellar', 0.12286970596222707),\n",
       " ('Unbroken', 0.11298392969275164),\n",
       " ('Guardians of the Galaxy', 0.090874959467711777),\n",
       " ('Divergent', 0.071129882767397401),\n",
       " ('The Hunger Games: Mockingjay - Part 1', 0.053533966431183633),\n",
       " ('The Lego Movie', 0.051121037070687873)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "recs[:10]"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Malificent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Question 2:  Which movie(s) would you most like to see, based on your classmates experience? (40 pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ad\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:6: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n"
     ]
    }
   ],
   "source": [
    "#HINTS...\n",
    "\n",
    "#the movies I've seen\n",
    "my_ratings = M.ix['Benjamin Baysinger'].dropna()\n",
    "#the movie I've rated the highest\n",
    "my_ratings.sort(ascending=False)\n",
    "#the name of the movie I've rated the highest\n",
    "fav_movie = my_ratings.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Gone Girl'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fav_movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [],
   "source": [
    "recs = get_recs('Gone Girl', M, 10)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
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    {
     "data": {
      "text/plain": [
       "[('The Fault in Our Stars', 0.14705909007675133),\n",
       " ('The Lego Movie', 0.1233839307397927),\n",
       " ('How to Train your Dragon 2', 0.11123592989213005),\n",
       " ('Godzilla', 0.095217741548246887),\n",
       " ('300: Rise of an Empire', 0.087816187972314844)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Lego Movie'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fav_movie = my_ratings.index[1]\n",
    "fav_movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Guardians of the Galaxy', 0.49569366865747871),\n",
       " ('How to Train your Dragon 2', 0.33027019377932165),\n",
       " ('Gone Girl', 0.1233839307397927),\n",
       " ('Transformers', 0.10488725566822023),\n",
       " ('Godzilla', 0.090051079891025232)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recs = get_recs('The Lego Movie', M, 10)\n",
    "recs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Guardians of the Galaxy'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fav_movie = my_ratings.index[2]\n",
    "fav_movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('The Lego Movie', 0.49569366865747871),\n",
       " ('The Hunger Games: Mockingjay - Part 1', 0.28727462661538056),\n",
       " ('How to Train your Dragon 2', 0.2226842108691125),\n",
       " ('Big Hero 6', 0.19018144357818259),\n",
       " ('Godzilla', 0.12329924047901797)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recs = get_recs('Guardians of the Galaxy', M, 10)\n",
    "recs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'American Sniper'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fav_movie = my_ratings.index[3]\n",
    "fav_movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Divergent', 0.023288517393348744),\n",
       " ('Gone Girl', 0.021549485282159685),\n",
       " ('Unbroken', 0.015613851961642226),\n",
       " ('Interstellar', 0.00027804492967911518),\n",
       " ('The Hobbit', 2.0064398342414436e-05)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recs = get_recs('American Sniper', M, 10)\n",
    "recs[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Movie Recommendations: Seriously! Divergent was an awful movie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<li> The Fault in Our Stars\n",
    "<li> Guardians of the Galaxy\n",
    "<li> The Lego Movie \n",
    "<li> Divergent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Question 3: Bonus Question...  For all the movies you haven't seen, can you predict how you'd rate them using your the class reviews? (10 pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "I have seen all the movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<li> Compute the mean value for each row   \n",
    "<li> Replace missing ratings with mean \n",
    "<li> Subtract mean from the row\n",
    "<li> Do steps 1-3 for all the rows\n",
    "<li> Compute the cosine similarity between the rows\n",
    "<li> Pick a neighboorhood size e.g. N=2\n",
    "<li> Compute the predicted rating\n",
    "<li> r(u,i)= Sigma(similarity (w,u)* rating(w,i))/Sigma(similarity(w,u))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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