INFOSYS_Stock_Market_Price_Prediction_KNN.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "##  INFOSYS STOCK MARKETING PRICE PREDICTION FROM [01-07-2015 to 01-07-2020(5 Years)]\n",
    "### DATASET FROM NSE India (National Stock Exchange of India Ltd)\n",
    "### K-NEAREST-NEIGHBORS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>494.5</td>\n",
       "      <td>502.500000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>498.700012</td>\n",
       "      <td>415.561249</td>\n",
       "      <td>6880852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>499.5</td>\n",
       "      <td>500.700012</td>\n",
       "      <td>492.524994</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>411.644745</td>\n",
       "      <td>4007568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-07-03</td>\n",
       "      <td>494.0</td>\n",
       "      <td>496.500000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>495.149994</td>\n",
       "      <td>412.603058</td>\n",
       "      <td>2695306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-07-06</td>\n",
       "      <td>492.5</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>487.500000</td>\n",
       "      <td>491.649994</td>\n",
       "      <td>409.686493</td>\n",
       "      <td>4305602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-07-07</td>\n",
       "      <td>492.5</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>489.500000</td>\n",
       "      <td>490.250000</td>\n",
       "      <td>408.519897</td>\n",
       "      <td>3497418.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open        High         Low       Close   Adj Close  \\\n",
       "0  2015-07-01  494.5  502.500000  493.000000  498.700012  415.561249   \n",
       "1  2015-07-02  499.5  500.700012  492.524994  494.000000  411.644745   \n",
       "2  2015-07-03  494.0  496.500000  491.000000  495.149994  412.603058   \n",
       "3  2015-07-06  492.5  494.000000  487.500000  491.649994  409.686493   \n",
       "4  2015-07-07  492.5  495.000000  489.500000  490.250000  408.519897   \n",
       "\n",
       "      Volume  \n",
       "0  6880852.0  \n",
       "1  4007568.0  \n",
       "2  2695306.0  \n",
       "3  4305602.0  \n",
       "4  3497418.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INFOSYS= pd.read_csv(\"C://Users//hp//Desktop//STOCK_MARKET//INFY.NS.csv\")\n",
    "INFOSYS = INFOSYS.dropna()\n",
    "INFOSYS = INFOSYS[['Date','Open', 'High', 'Low','Close','Adj Close','Volume']]\n",
    "INFOSYS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1227.000000</td>\n",
       "      <td>1.227000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>605.359984</td>\n",
       "      <td>611.994194</td>\n",
       "      <td>598.518723</td>\n",
       "      <td>605.161715</td>\n",
       "      <td>557.417386</td>\n",
       "      <td>8.675740e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>103.981567</td>\n",
       "      <td>105.130168</td>\n",
       "      <td>103.068678</td>\n",
       "      <td>104.111674</td>\n",
       "      <td>118.345056</td>\n",
       "      <td>7.941786e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>443.799988</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>436.700012</td>\n",
       "      <td>390.604187</td>\n",
       "      <td>7.589560e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>514.187500</td>\n",
       "      <td>519.762482</td>\n",
       "      <td>509.349991</td>\n",
       "      <td>513.974976</td>\n",
       "      <td>451.269745</td>\n",
       "      <td>5.361510e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>586.025024</td>\n",
       "      <td>592.450012</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>585.950012</td>\n",
       "      <td>517.107422</td>\n",
       "      <td>7.023516e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>704.924988</td>\n",
       "      <td>711.725006</td>\n",
       "      <td>695.975006</td>\n",
       "      <td>704.262512</td>\n",
       "      <td>676.590088</td>\n",
       "      <td>9.551616e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>843.299988</td>\n",
       "      <td>847.000000</td>\n",
       "      <td>835.049988</td>\n",
       "      <td>840.150024</td>\n",
       "      <td>810.035217</td>\n",
       "      <td>1.644050e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close  \\\n",
       "count  1227.000000  1227.000000  1227.000000  1227.000000  1227.000000   \n",
       "mean    605.359984   611.994194   598.518723   605.161715   557.417386   \n",
       "std     103.981567   105.130168   103.068678   104.111674   118.345056   \n",
       "min     440.000000   443.799988   430.000000   436.700012   390.604187   \n",
       "25%     514.187500   519.762482   509.349991   513.974976   451.269745   \n",
       "50%     586.025024   592.450012   580.000000   585.950012   517.107422   \n",
       "75%     704.924988   711.725006   695.975006   704.262512   676.590088   \n",
       "max     843.299988   847.000000   835.049988   840.150024   810.035217   \n",
       "\n",
       "             Volume  \n",
       "count  1.227000e+03  \n",
       "mean   8.675740e+06  \n",
       "std    7.941786e+06  \n",
       "min    7.589560e+05  \n",
       "25%    5.361510e+06  \n",
       "50%    7.023516e+06  \n",
       "75%    9.551616e+06  \n",
       "max    1.644050e+08  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INFOSYS.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1227 entries, 0 to 1227\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       1227 non-null   object \n",
      " 1   Open       1227 non-null   float64\n",
      " 2   High       1227 non-null   float64\n",
      " 3   Low        1227 non-null   float64\n",
      " 4   Close      1227 non-null   float64\n",
      " 5   Adj Close  1227 non-null   float64\n",
      " 6   Volume     1227 non-null   float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 76.7+ KB\n"
     ]
    }
   ],
   "source": [
    "INFOSYS.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
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       "        </script>\n",
       "        "
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     "metadata": {},
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   ],
   "source": [
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import plot\n",
    "\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout = go.Layout(\n",
    "    title='STOCK PRICE OF INFOSYS',\n",
    "        xaxis=dict(\n",
    "            title='date',\n",
    "            titlefont=dict(\n",
    "                family='Courier New, monospace',\n",
    "                size=18,\n",
    "                color='blue'\n",
    "            )\n",
    "        ),\n",
    "        yaxis=dict(\n",
    "            title='Price',\n",
    "            titlefont=dict(\n",
    "                family='Courier New, monospace',\n",
    "                size=18,\n",
    "                color='red'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "INFOSYS_DATA = [{'x':INFOSYS['Date'], 'y':INFOSYS['Close']}]\n",
    "plot = go.Figure(data=INFOSYS_DATA, layout=layout)"
   ]
  },
  {
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   "execution_count": 30,
   "metadata": {},
   "outputs": [
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          "2015-07-01",
          "2015-07-02",
          "2015-07-03",
          "2015-07-06",
          "2015-07-07",
          "2015-07-08",
          "2015-07-09",
          "2015-07-10",
          "2015-07-13",
          "2015-07-14",
          "2015-07-15",
          "2015-07-16",
          "2015-07-17",
          "2015-07-20",
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       "    .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>Open-Close</th>\n",
       "      <th>High-Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.200012</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-5.500000</td>\n",
       "      <td>8.175018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.149994</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.850006</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.250000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Open-Close  High-Low\n",
       "0    4.200012  9.500000\n",
       "1   -5.500000  8.175018\n",
       "2    1.149994  5.500000\n",
       "3   -0.850006  6.500000\n",
       "4   -2.250000  5.500000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INFOSYS['Open-Close']= INFOSYS.Close - INFOSYS.Open\n",
    "INFOSYS['High-Low']  = INFOSYS.High - INFOSYS.Low\n",
    "INFOSYS = INFOSYS.dropna()\n",
    "X= INFOSYS[['Open-Close', 'High-Low']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y= np.where(INFOSYS['Close'].shift(-1)>INFOSYS['Close'],1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_percentage = 0.7\n",
    "split = int(split_percentage*len(INFOSYS))\n",
    "\n",
    "X_train = X[:split]\n",
    "Y_train = Y[:split]\n",
    "\n",
    "X_test = X[split:]\n",
    "Y_test = Y[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train_data Accuracy: 0.63\n",
      "Test_data Accuracy: 0.43\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "\n",
    "knn.fit(X_train, Y_train)\n",
    "\n",
    "accuracy_train = accuracy_score(Y_train, knn.predict(X_train))\n",
    "accuracy_test = accuracy_score(Y_test, knn.predict(X_test))\n",
    "\n",
    "print ('Train_data Accuracy: %.2f' %accuracy_train)\n",
    "print ('Test_data Accuracy: %.2f' %accuracy_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "INFOSYS['Predicted_Signal'] = knn.predict(X)\n",
    "\n",
    "INFOSYS['SPY_returns'] = np.log(INFOSYS['Close']/INFOSYS['Close'].shift(1))\n",
    "Cumulative_SPY_returns = INFOSYS[split:]['SPY_returns'].cumsum()*100\n",
    " \n",
    "INFOSYS['Startegy_returns'] = INFOSYS['SPY_returns']* INFOSYS['Predicted_Signal'].shift(1)\n",
    "Cumulative_Strategy_returns = INFOSYS[split:]['Startegy_returns'].cumsum()*100\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(Cumulative_SPY_returns, color='r',label = 'SPY Returns')\n",
    "plt.plot(Cumulative_Strategy_returns, color='g', label = 'Strategy Returns')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe ratio: -0.92\n"
     ]
    }
   ],
   "source": [
    "Std = Cumulative_Strategy_returns.std()\n",
    "Sharpe = (Cumulative_Strategy_returns-Cumulative_SPY_returns)/Std\n",
    "Sharpe = Sharpe.mean()\n",
    "print ('Sharpe ratio: %.2f'%Sharpe )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = knn.fit(X_train, Y_train)\n",
    "model = model.fit (X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.66666667 0.33333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.4        0.6       ]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.6        0.4       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.26666667 0.73333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.6        0.4       ]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.6        0.4       ]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.66666667 0.33333333]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.73333333 0.26666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
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      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.2        0.8       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.66666667 0.33333333]\n",
      " [0.4        0.6       ]\n",
      " [0.26666667 0.73333333]\n",
      " [0.6        0.4       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.4        0.6       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.66666667 0.33333333]\n",
      " [0.4        0.6       ]\n",
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      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
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      " [0.73333333 0.26666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.26666667 0.73333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.4        0.6       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
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      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.8        0.2       ]\n",
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      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.6        0.4       ]\n",
      " [0.8        0.2       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.73333333 0.26666667]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.2        0.8       ]\n",
      " [0.26666667 0.73333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.13333333 0.86666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.4        0.6       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.66666667 0.33333333]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.6        0.4       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.13333333 0.86666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.13333333 0.86666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.4        0.6       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.66666667 0.33333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.6        0.4       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.66666667 0.33333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.66666667 0.33333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.26666667 0.73333333]\n",
      " [0.2        0.8       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.73333333 0.26666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.26666667 0.73333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.2        0.8       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.6        0.4       ]\n",
      " [0.4        0.6       ]\n",
      " [0.26666667 0.73333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.73333333 0.26666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.2        0.8       ]\n",
      " [0.2        0.8       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.6        0.4       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.73333333 0.26666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.6        0.4       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.33333333 0.66666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.26666667 0.73333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]\n",
      " [0.6        0.4       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.66666667 0.33333333]\n",
      " [0.6        0.4       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.6        0.4       ]\n",
      " [0.66666667 0.33333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.8        0.2       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.66666667 0.33333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.33333333 0.66666667]\n",
      " [0.66666667 0.33333333]\n",
      " [0.8        0.2       ]\n",
      " [0.6        0.4       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.73333333 0.26666667]\n",
      " [0.4        0.6       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.6        0.4       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.4        0.6       ]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.4        0.6       ]\n",
      " [0.4        0.6       ]\n",
      " [0.6        0.4       ]\n",
      " [0.13333333 0.86666667]\n",
      " [0.2        0.8       ]\n",
      " [0.4        0.6       ]\n",
      " [0.33333333 0.66666667]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.46666667 0.53333333]\n",
      " [0.13333333 0.86666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.6        0.4       ]\n",
      " [0.2        0.8       ]\n",
      " [0.2        0.8       ]\n",
      " [0.46666667 0.53333333]\n",
      " [0.6        0.4       ]\n",
      " [0.26666667 0.73333333]\n",
      " [0.53333333 0.46666667]\n",
      " [0.53333333 0.46666667]\n",
      " [0.26666667 0.73333333]]\n"
     ]
    }
   ],
   "source": [
    "probability = model.predict_proba(X_test)\n",
    "print(probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 65 110]\n",
      " [ 99  95]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.40      0.37      0.38       175\n",
      "           1       0.46      0.49      0.48       194\n",
      "\n",
      "    accuracy                           0.43       369\n",
      "   macro avg       0.43      0.43      0.43       369\n",
      "weighted avg       0.43      0.43      0.43       369\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6317016317016317\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train,Y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ACCURACY OF TRAINING MODEL FOR INFOSYS STOCK MARKET PRICE PREDICTION IS ABOUT 63%."
   ]
  }
 ],
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