diff --git a/task_6/task6.ipynb b/task_6/task6.ipynb
new file mode 100644
index 0000000..3df9199
--- /dev/null
+++ b/task_6/task6.ipynb
@@ -0,0 +1,263 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Untitled3.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ogz5nQZfV398",
+        "outputId": "f4e359e6-0c7a-48b6-f66d-15d39b9e8896",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118
+        }
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import datetime as dt\n",
+        "import requests\n",
+        "\n",
+        "from datetime import datetime as dt\n",
+        "\n",
+        "datay='https://cloud.iexapis.com/stable/stock/aapl/chart/1y?token=pk_a4d36832d58c4fc5aef7b5ee188d48ce'\n",
+        "datad='https://cloud.iexapis.com/stable/stock/aapl/chart/1d?token=pk_a4d36832d58c4fc5aef7b5ee188d48ce'\n",
+        "dataday='https://cloud.iexapis.com/stable/stock/aapl/intraday-prices/batch?token=pk_a4d36832d58c4fc5aef7b5ee188d48ce'\n",
+        "import json\n",
+        "dd= requests.get(datad)\n",
+        "dy= requests.get(datay)\n",
+        "dday= requests.get(dataday)\n",
+        "datad=json.loads(dd.text)\n",
+        "datay=json.loads(dy.text)\n",
+        "dataday=json.loads(dday.text)\n",
+        "for i in datay:\n",
+        "    i['minute'] = i.get('minute','09:30')\n",
+        "for i in datay:\n",
+        "      string= i['date']+'T'+i['minute']+':00+00:00'\n",
+        "      element= dt.strptime(string,\"%Y-%m-%dT%H:%M:%S+00:00\")\n",
+        "      timestamp= dt.timestamp(element)\n",
+        "      i['timestamps']= i.get('timestamps',timestamp)\n",
+        "for i in datad:\n",
+        "       string= i['date']+'T'+i['minute']+':00+00:00'\n",
+        "       element= dt.strptime(string,\"%Y-%m-%dT%H:%M:%S+00:00\")\n",
+        "       timestamp= dt.timestamp(element)     \n",
+        "       i['timestamps']= i.get('timestamps', timestamp)\n",
+        "for i in dataday:\n",
+        "        string= i['date']+'T'+i['minute']+':00+00:00'\n",
+        "        element= dt.strptime(string,\"%Y-%m-%dT%H:%M:%S+00:00\")\n",
+        "        timestamp= dt.timestamp(element)\n",
+        "        i['timestamps']= i.get('timestamps', timestamp)\n",
+        "      \n",
+        "df1= pd.DataFrame(datay)\n",
+        "df2= pd.DataFrame(datad)\n",
+        "df3= pd.DataFrame(dataday)\n",
+        "      \n",
+        "frames =[df1,df2,df3]\n",
+        "final =pd.concat(frames)\n",
+        "final= final.drop(['date','close','high','low','volume','uOpen','uHigh','uLow','uVolume','change','label','changeOverTime','minute','uClose','changePercent','average','notional','numberOfTrades'],axis=1)\n",
+        "print(final.head())"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "    open    timestamps\n",
+            "0  56.98  1.570700e+09\n",
+            "1  58.24  1.570786e+09\n",
+            "2  58.73  1.571045e+09\n",
+            "3  59.10  1.571132e+09\n",
+            "4  58.34  1.571218e+09\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "41n4oqvKX3iH"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.model_selection import KFold\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.linear_model import Lasso\n",
+        "from sklearn.linear_model import ElasticNet\n",
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "from sklearn.ensemble import GradientBoostingRegressor\n",
+        "from sklearn.svm import SVR\n",
+        "from sklearn.metrics import mean_squared_error, r2_score"
+      ],
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DzvIijfJialI",
+        "outputId": "90729ba7-6e80-443b-ff5c-2375c640c945",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118
+        }
+      },
+      "source": [
+        "array = final.values\n",
+        "X = array[:,1]\n",
+        "X = X.reshape(-1,1)\n",
+        "y = array[:,0]\n",
+        "X_train, X_val, y_train, y_val = train_test_split(X, y,test_size=0.2, random_state=3)\n",
+        "\n",
+        "models = []\n",
+        "models.append((' LR ', LinearRegression()))\n",
+        "models.append((' LASSO ', Lasso()))\n",
+        "models.append((' EN ', ElasticNet()))\n",
+        "models.append((' KNN ', KNeighborsRegressor()))\n",
+        "models.append((' CART ', DecisionTreeRegressor()))\n",
+        "models.append((' SVR ', SVR()))\n",
+        "\n",
+        "results = []\n",
+        "names = []\n",
+        "\n",
+        "for name, model in models:\n",
+        "    kfold = KFold(n_splits=3, random_state=1, shuffle=True)\n",
+        "    cv_score = cross_val_score(\n",
+        "        model, X_train, y_train, cv=kfold, scoring='r2')\n",
+        "    results.append(cv_score)\n",
+        "    names.append(name)\n",
+        "    scores = \"%s: %f (%f)\" % (name, cv_score.mean(), cv_score.std())\n",
+        "    print(scores)"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            " LR : 0.907492 (0.015514)\n",
+            " LASSO : 0.907492 (0.015514)\n",
+            " EN : 0.907492 (0.015514)\n",
+            " KNN : 0.993213 (0.000289)\n",
+            " CART : 0.993502 (0.002498)\n",
+            " SVR : 0.969242 (0.006306)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "e7-rMb2EireC",
+        "outputId": "9c4fcdff-253a-4d61-d11b-a351f62f79f5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        }
+      },
+      "source": [
+        "fig = plt.figure()\n",
+        "fig.suptitle(' Comparison of various Algos ')\n",
+        "ax = fig.add_subplot(111)\n",
+        "plt.boxplot(results)\n",
+        "ax.set_xticklabels(names)\n",
+        "plt.show()"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nLe6srCyi1if",
+        "outputId": "7fb7b027-9b1e-477c-c452-ba994dd1ed9e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "model = DecisionTreeRegressor(max_depth=20)\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "predictions = model.predict(X_val)\n",
+        "print(r2_score(y_val, predictions))"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "0.9940250764416401\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jukPfF6zi-qC",
+        "outputId": "6a2a9d57-e68a-4a67-be45-ef49fd14a719",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 677
+        }
+      },
+      "source": [
+        "figure = plt.figure(figsize=(20,10))\n",
+        "plt.plot(X, y, label=\"ACTUAL\")\n",
+        "plt.plot(X, model.predict(X.reshape(-1,1)), label=\"PREDICTION\")\n",
+        "plt.legend(loc='lower right')\n",
+        "plt.title(\"STOCK PRICE\", fontsize=16, fontweight='bold')\n",
+        "plt.suptitle(\"STOCK PRICE PREDICTION\", fontsize=10)\n",
+        "plt.xlabel(\"TIME INTERVAL\")\n",
+        "plt.ylabel(\"PRICE OF STOCK\")\n",
+        "plt.show()\n"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1440x720 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file