INFOSYS_Stock_Market_Price_Prediction_Random_Forest_Classifier (1).ipynb

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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",
    "### Random_Forest_Classifier"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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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": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
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       "  <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",
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       "  <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",
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      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "INFOSYS.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
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       "        </script>\n",
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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": 7,
   "metadata": {},
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    "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",
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    "                size=18,\n",
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    "        )\n",
    "    )\n",
    ")\n",
    "INFOSYS_DATA = [{'x':INFOSYS['Date'], 'y':INFOSYS['Close']}]\n",
    "plot = go.Figure(data=INFOSYS_DATA, layout=layout)"
   ]
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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",
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          "2015-07-13",
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          "2015-07-15",
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       "        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": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y= np.where(INFOSYS['Close'].shift(-1)>INFOSYS['Close'],1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_percentage = 0.8\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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "\n",
    "for num_trees in range(1,41):\n",
    "    clf = RandomForestClassifier(n_estimators = num_trees)\n",
    "    scores.append(cross_val_score(clf, X, Y, cv=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.50406504 0.4796748  0.49593496 0.46341463 0.56910569 0.56910569\n",
      " 0.44715447 0.52459016 0.47540984 0.45081967]\n"
     ]
    }
   ],
   "source": [
    "print(scores[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.56910569 0.46341463 0.48780488 0.52845528 0.45528455 0.46341463\n",
      " 0.49593496 0.50819672 0.55737705 0.48360656]\n"
     ]
    }
   ],
   "source": [
    "print(scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=16,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.40      0.44      0.42       116\n",
      "           1       0.46      0.42      0.44       130\n",
      "\n",
      "    accuracy                           0.43       246\n",
      "   macro avg       0.43      0.43      0.43       246\n",
      "weighted avg       0.43      0.43      0.43       246\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test, rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[51 65]\n",
      " [75 55]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Y_test, rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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'] = rfc.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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe ratio: 0.09\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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = rfc.fit(X_train, Y_train)\n",
    "model = rfc.fit (X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [0.125  0.875 ]\n",
      " [0.375  0.625 ]\n",
      " [0.625  0.375 ]\n",
      " [0.375  0.625 ]\n",
      " [0.5    0.5   ]\n",
      " [0.125  0.875 ]\n",
      " [0.125  0.875 ]\n",
      " [1.     0.    ]\n",
      " [0.625  0.375 ]\n",
      " [0.625  0.375 ]\n",
      " [0.0625 0.9375]\n",
      " [0.375  0.625 ]\n",
      " [0.25   0.75  ]\n",
      " [0.375  0.625 ]\n",
      " [0.4375 0.5625]\n",
      " [0.8125 0.1875]\n",
      " [0.8125 0.1875]\n",
      " [0.4375 0.5625]\n",
      " [0.125  0.875 ]\n",
      " [0.125  0.875 ]\n",
      " [0.125  0.875 ]\n",
      " [0.375  0.625 ]\n",
      " [0.8125 0.1875]\n",
      " [0.5    0.5   ]\n",
      " [0.1875 0.8125]\n",
      " [0.6875 0.3125]\n",
      " [0.5    0.5   ]\n",
      " [0.0625 0.9375]\n",
      " [0.4375 0.5625]\n",
      " [0.375  0.625 ]\n",
      " [0.6875 0.3125]\n",
      " [0.75   0.25  ]\n",
      " [0.     1.    ]\n",
      " [0.5625 0.4375]\n",
      " [0.75   0.25  ]\n",
      " [0.     1.    ]\n",
      " [0.875  0.125 ]\n",
      " [0.875  0.125 ]\n",
      " [0.4375 0.5625]\n",
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      " [0.25   0.75  ]\n",
      " [0.4375 0.5625]\n",
      " [0.375  0.625 ]\n",
      " [0.625  0.375 ]\n",
      " [0.75   0.25  ]\n",
      " [0.125  0.875 ]\n",
      " [0.75   0.25  ]\n",
      " [0.6875 0.3125]\n",
      " [0.125  0.875 ]\n",
      " [0.375  0.625 ]\n",
      " [0.5625 0.4375]\n",
      " [0.25   0.75  ]\n",
      " [0.1875 0.8125]\n",
      " [0.5625 0.4375]\n",
      " [0.6875 0.3125]\n",
      " [0.6875 0.3125]\n",
      " [0.875  0.125 ]\n",
      " [0.875  0.125 ]\n",
      " [0.6875 0.3125]\n",
      " [0.0625 0.9375]\n",
      " [0.     1.    ]\n",
      " [0.     1.    ]\n",
      " [0.6875 0.3125]\n",
      " [0.5625 0.4375]\n",
      " [0.3125 0.6875]\n",
      " [0.0625 0.9375]\n",
      " [0.4375 0.5625]\n",
      " [0.625  0.375 ]\n",
      " [0.5    0.5   ]\n",
      " [0.1875 0.8125]\n",
      " [0.375  0.625 ]\n",
      " [0.25   0.75  ]\n",
      " [0.6875 0.3125]\n",
      " [0.5625 0.4375]\n",
      " [0.625  0.375 ]\n",
      " [0.6875 0.3125]\n",
      " [0.25   0.75  ]\n",
      " [0.5    0.5   ]\n",
      " [0.5625 0.4375]\n",
      " [0.3125 0.6875]\n",
      " [0.9375 0.0625]\n",
      " [0.3125 0.6875]\n",
      " [0.875  0.125 ]\n",
      " [0.375  0.625 ]\n",
      " [0.875  0.125 ]\n",
      " [0.4375 0.5625]\n",
      " [0.5    0.5   ]\n",
      " [0.5625 0.4375]\n",
      " [0.375  0.625 ]\n",
      " [0.5    0.5   ]\n",
      " [0.5    0.5   ]\n",
      " [0.375  0.625 ]\n",
      " [0.875  0.125 ]\n",
      " [0.625  0.375 ]\n",
      " [0.1875 0.8125]\n",
      " [0.75   0.25  ]\n",
      " [0.1875 0.8125]\n",
      " [0.4375 0.5625]\n",
      " [0.3125 0.6875]\n",
      " [0.8125 0.1875]\n",
      " [0.1875 0.8125]\n",
      " [0.25   0.75  ]\n",
      " [0.4375 0.5625]\n",
      " [0.5    0.5   ]\n",
      " [0.5625 0.4375]\n",
      " [0.625  0.375 ]\n",
      " [0.375  0.625 ]\n",
      " [0.25   0.75  ]\n",
      " [0.875  0.125 ]\n",
      " [0.25   0.75  ]\n",
      " [0.625  0.375 ]\n",
      " [0.3125 0.6875]\n",
      " [0.8125 0.1875]\n",
      " [0.375  0.625 ]\n",
      " [0.375  0.625 ]\n",
      " [0.5    0.5   ]\n",
      " [0.125  0.875 ]\n",
      " [0.25   0.75  ]\n",
      " [0.375  0.625 ]\n",
      " [0.25   0.75  ]\n",
      " [0.0625 0.9375]\n",
      " [0.5625 0.4375]\n",
      " [0.375  0.625 ]\n",
      " [0.75   0.25  ]\n",
      " [0.     1.    ]\n",
      " [0.3125 0.6875]\n",
      " [0.     1.    ]\n",
      " [0.25   0.75  ]\n",
      " [0.6875 0.3125]\n",
      " [0.4375 0.5625]\n",
      " [0.625  0.375 ]\n",
      " [0.3125 0.6875]\n",
      " [0.75   0.25  ]\n",
      " [0.625  0.375 ]\n",
      " [0.375  0.625 ]\n",
      " [0.3125 0.6875]\n",
      " [0.25   0.75  ]\n",
      " [0.4375 0.5625]\n",
      " [0.1875 0.8125]\n",
      " [0.3125 0.6875]\n",
      " [0.625  0.375 ]\n",
      " [0.875  0.125 ]\n",
      " [0.5625 0.4375]\n",
      " [0.1875 0.8125]\n",
      " [0.1875 0.8125]\n",
      " [0.5    0.5   ]\n",
      " [0.5625 0.4375]\n",
      " [0.25   0.75  ]\n",
      " [0.6875 0.3125]\n",
      " [0.1875 0.8125]\n",
      " [0.75   0.25  ]\n",
      " [0.3125 0.6875]\n",
      " [0.6875 0.3125]\n",
      " [0.4375 0.5625]\n",
      " [0.5    0.5   ]\n",
      " [0.625  0.375 ]\n",
      " [0.6875 0.3125]\n",
      " [0.4375 0.5625]\n",
      " [0.5625 0.4375]\n",
      " [0.4375 0.5625]\n",
      " [0.125  0.875 ]\n",
      " [0.3125 0.6875]\n",
      " [0.625  0.375 ]\n",
      " [0.6875 0.3125]\n",
      " [0.9375 0.0625]\n",
      " [0.25   0.75  ]\n",
      " [0.375  0.625 ]\n",
      " [0.625  0.375 ]\n",
      " [0.1875 0.8125]\n",
      " [0.6875 0.3125]\n",
      " [0.5625 0.4375]\n",
      " [0.625  0.375 ]\n",
      " [0.8125 0.1875]\n",
      " [0.625  0.375 ]\n",
      " [0.625  0.375 ]\n",
      " [0.625  0.375 ]\n",
      " [0.6875 0.3125]\n",
      " [0.625  0.375 ]\n",
      " [0.8125 0.1875]\n",
      " [0.8125 0.1875]\n",
      " [0.9375 0.0625]\n",
      " [0.9375 0.0625]\n",
      " [0.5    0.5   ]\n",
      " [1.     0.    ]\n",
      " [0.25   0.75  ]\n",
      " [0.625  0.375 ]\n",
      " [0.625  0.375 ]\n",
      " [0.8125 0.1875]\n",
      " [0.8125 0.1875]\n",
      " [0.3125 0.6875]\n",
      " [0.75   0.25  ]\n",
      " [0.3125 0.6875]\n",
      " [0.9375 0.0625]\n",
      " [0.0625 0.9375]\n",
      " [0.6875 0.3125]\n",
      " [0.625  0.375 ]\n",
      " [0.8125 0.1875]\n",
      " [0.5    0.5   ]\n",
      " [0.25   0.75  ]\n",
      " [0.75   0.25  ]\n",
      " [0.125  0.875 ]\n",
      " [0.4375 0.5625]\n",
      " [0.6875 0.3125]\n",
      " [0.3125 0.6875]\n",
      " [0.75   0.25  ]\n",
      " [0.25   0.75  ]\n",
      " [0.1875 0.8125]\n",
      " [0.8125 0.1875]\n",
      " [0.625  0.375 ]\n",
      " [1.     0.    ]\n",
      " [0.25   0.75  ]\n",
      " [0.5625 0.4375]\n",
      " [0.125  0.875 ]\n",
      " [0.625  0.375 ]\n",
      " [0.5625 0.4375]\n",
      " [0.5    0.5   ]\n",
      " [0.4375 0.5625]\n",
      " [0.8125 0.1875]\n",
      " [0.125  0.875 ]\n",
      " [0.8125 0.1875]\n",
      " [0.5    0.5   ]\n",
      " [0.375  0.625 ]\n",
      " [0.5    0.5   ]\n",
      " [0.5    0.5   ]\n",
      " [0.9375 0.0625]\n",
      " [0.1875 0.8125]\n",
      " [0.1875 0.8125]\n",
      " [0.5    0.5   ]\n",
      " [0.1875 0.8125]\n",
      " [0.5    0.5   ]\n",
      " [0.625  0.375 ]\n",
      " [0.125  0.875 ]\n",
      " [0.0625 0.9375]\n",
      " [0.5625 0.4375]\n",
      " [0.375  0.625 ]\n",
      " [0.125  0.875 ]\n",
      " [0.125  0.875 ]\n",
      " [0.125  0.875 ]\n",
      " [0.9375 0.0625]\n",
      " [0.1875 0.8125]\n",
      " [0.8125 0.1875]\n",
      " [0.6875 0.3125]\n",
      " [0.     1.    ]]\n"
     ]
    }
   ],
   "source": [
    "probability = model.predict_proba(X_test)\n",
    "print(probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[55 61]\n",
      " [74 56]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.43      0.47      0.45       116\n",
      "           1       0.48      0.43      0.45       130\n",
      "\n",
      "    accuracy                           0.45       246\n",
      "   macro avg       0.45      0.45      0.45       246\n",
      "weighted avg       0.45      0.45      0.45       246\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9806320081549439\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 98%."
   ]
  }
 ],
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