INFOSYS_Stock_Market_Price_Prediction_DecisionTreeClassifier.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",
    "### Decision_Tree_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",
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 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": [
    {
     "data": {
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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",
       "    </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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [
    {
     "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",
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       "        requirejs.config({\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": 7,
   "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": 8,
   "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",
          "2015-07-21",
          "2015-07-22",
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       "        vertical-align: top;\n",
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       "\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['Adj Close'].shift(-1)>INFOSYS['Adj 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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features=None, max_leaf_nodes=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, presort='deprecated',\n",
      "                       random_state=None, splitter='best')\n",
      "Train_data Accuracy: 1.00\n",
      "Test_data Accuracy: 0.54\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "%pylab inline\n",
    "%matplotlib inline\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, Y_train)\n",
    "print(model)\n",
    "\n",
    "accuracy_train = accuracy_score(Y_train, model.predict(X_train))\n",
    "accuracy_test = accuracy_score(Y_test, model.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": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "probability = model.predict_proba(X_test)\n",
    "print(probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[64 52]\n",
      " [61 69]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.51      0.55      0.53       116\n",
      "           1       0.57      0.53      0.55       130\n",
      "\n",
      "    accuracy                           0.54       246\n",
      "   macro avg       0.54      0.54      0.54       246\n",
      "weighted avg       0.54      0.54      0.54       246\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(Y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\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 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_line = Pipeline([('clf', DecisionTreeClassifier())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('clf',\n",
       "                 DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
       "                                        criterion='gini', max_depth=None,\n",
       "                                        max_features=None, max_leaf_nodes=None,\n",
       "                                        min_impurity_decrease=0.0,\n",
       "                                        min_impurity_split=None,\n",
       "                                        min_samples_leaf=1, min_samples_split=2,\n",
       "                                        min_weight_fraction_leaf=0.0,\n",
       "                                        presort='deprecated', random_state=None,\n",
       "                                        splitter='best'))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_line.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_line.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = cross_val_score(estimator=pipe_line, X=X, y=Y, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv accuracy score : [0.45528455 0.49593496 0.5203252  0.5203252  0.5203252  0.47154472\n",
      " 0.43902439 0.50819672 0.48360656 0.52459016]\n",
      "cv accuracy : 0.494 +/- 0.029\n"
     ]
    }
   ],
   "source": [
    "print('cv accuracy score : %s' % score)\n",
    "print('cv accuracy : %.3f +/- %.3f' % (np.mean(score), np.std(score)))"
   ]
  }
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