Prod_simil.ipynb

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  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Info ?!You are probably familiar with the idea that transitions may be needed between paragraphs or sections in a paper (see our handout on transitions). Sometimes they are also helpful within the body of a single paragraph. Within a paragraph, transitions are often single words or short phrases that help to establish relationships between ideas and to create a logical progression of those ideas in a paragraph. This is especially likely to be true within paragraphs that discuss multiple examples. Let’s take a look at a version of our piranha paragraph that uses transitions to orient the reader:\n",
      "Product 2 Info ?!Although most people consider piranhas to be quite dangerous, they are, except in two main situations, entirely harmless. Piranhas rarely feed on large animals; they eat smaller fish and aquatic plants. When confronted with humans, piranhas’ instinct is to flee, not attack. But there are two situations in which a piranha bite is likely. The first is when a frightened piranha is lifted out of the water—for example, if it has been caught in a fishing net. The second is when the water level in pools where piranhas are living falls too low. A large number of fish may be trapped in a single pool, and if they are hungry, they may attack anything that enters the water.\n"
     ]
    }
   ],
   "source": [
    "a = input(\"Product Info ?!\")\n",
    "b = input(\"Product 2 Info ?!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [{'Desc': a}, {'Desc': b}]\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Desc</th>\n",
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       "      <td>You are probably familiar with the idea that t...</td>\n",
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       "      <th>1</th>\n",
       "      <td>Although most people consider piranhas to be q...</td>\n",
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       "                                                Desc\n",
       "0  You are probably familiar with the idea that t...\n",
       "1  Although most people consider piranhas to be q..."
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf = TfidfVectorizer(analyzer='word', ngram_range=(0,1), min_df=0, stop_words='english')\n",
    "tfidf_matrix = tf.fit_transform(df['Desc'])\n",
    "\n",
    "cosine_similarities = linear_kernel(tfidf_matrix, tfidf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_matrix = tfidf_matrix.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
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       "      <td>0.11782</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.16766</td>\n",
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       "      <td>0.471282</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.111777</td>\n",
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       "<p>2 rows × 78 columns</p>\n",
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       "         0         1         2         3        4         5         6   \\\n",
       "0  0.000000  0.000000  0.000000  0.000000  0.11782  0.000000  0.000000   \n",
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       "\n",
       "         7        8         9    ...          68        69        70  \\\n",
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       "1  0.111777  0.00000  0.111777   ...     0.07953  0.223554  0.111777   \n",
       "\n",
       "         71        72       73       74       75        76       77  \n",
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       "\n",
       "[2 rows x 78 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_df = pd.DataFrame(tfidf_matrix)\n",
    "tfidf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "simil = pd.DataFrame(cosine_similarities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
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       "          0         1\n",
       "0  1.000000  0.033335\n",
       "1  0.033335  1.000000"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "simil[simil.select_dtypes(include=['number']).columns] *= 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "            0           1\n",
       "0  100.000000    3.333527\n",
       "1    3.333527  100.000000"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "simil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = simil.loc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both the products are 3.333527 % similar\n"
     ]
    }
   ],
   "source": [
    "print(\"Both the products are %f\"%(x) +\" %\"+ \" similar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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