Stock_market_prediction.ipynb

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        "#IMPORT LIBRARIES\n",
        "import math\n",
        "import datetime\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import pandas_datareader as web\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,LSTM\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')"
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        "bold text"
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      "source": [
        "ticker=yf.Ticker('AAPL')\n",
        "df=yf.download(\"AAPL\",start='2005-01-13',end='2023-05-30',interval='1d')\n",
        "df"
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              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2005-01-13    1.316250    1.328929    1.245179    1.246429    1.057965   \n",
              "2005-01-14    1.254464    1.280714    1.235536    1.253571    1.064026   \n",
              "2005-01-18    1.247321    1.262500    1.209821    1.261607    1.070848   \n",
              "2005-01-19    1.258750    1.276071    1.245536    1.247857    1.059177   \n",
              "2005-01-20    1.243750    1.272679    1.240536    1.258214    1.067968   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2023-05-22  173.979996  174.710007  173.449997  174.199997  173.965073   \n",
              "2023-05-23  173.130005  173.380005  171.279999  171.559998  171.328644   \n",
              "2023-05-24  171.089996  172.419998  170.520004  171.839996  171.608261   \n",
              "2023-05-25  172.410004  173.899994  171.690002  172.990005  172.756714   \n",
              "2023-05-26  173.320007  175.770004  173.110001  175.429993  175.193420   \n",
              "\n",
              "                Volume  \n",
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              "2023-05-24    45143500  \n",
              "2023-05-25    56058300  \n",
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              "      <th>2005-01-13</th>\n",
              "      <td>1.316250</td>\n",
              "      <td>1.328929</td>\n",
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        "plt.figure(figsize=(16,8))\n",
        "plt.title('Close Price History',fontsize=29)\n",
        "plt.plot(df['Close'],alpha=1,color='deeppink',)\n",
        "plt.xlabel('Date',fontsize=29)\n",
        "plt.ylabel('Close Price USD($)',fontsize=29)\n",
        "plt.xticks(fontsize=29)\n",
        "plt.yticks(fontsize=29)\n",
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              "<Figure size 1600x800 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a new dataframe for close price\n",
        "data=df.filter(['Close'])\n",
        "#convert dataframe into numpy array\n",
        "dataset=data.values\n",
        "#get number of rows and training the model\n",
        "training_data_len=math.ceil(len(dataset)*.8)\n",
        "training_data_len\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5C2L5Phr_ilU",
        "outputId": "87cc95cd-f358-40d9-f58b-70c21b16b54f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3700"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#scale the data\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data=scaler.fit_transform(dataset)\n",
        "scaled_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZvDul6xCfd8",
        "outputId": "c00ac4ab-ab75-4d34-9bf6-e146f1a3cdcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.52108812e-04],\n",
              "       [1.91613347e-04],\n",
              "       [2.36062542e-04],\n",
              "       ...,\n",
              "       [9.43747230e-01],\n",
              "       [9.50108215e-01],\n",
              "       [9.63604386e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create the training dataset\n",
        "#create the scaled training dataset\n",
        "train_data=scaled_data[0:training_data_len]\n",
        "#we have to train the model\n",
        "x_train=[]\n",
        "y_train=[]\n",
        "\n",
        "for i in range(60,len(train_data)):\n",
        "  x_train.append(train_data[i-60:i,0])\n",
        "  y_train.append(train_data[i,0])\n",
        "  if i<=61:\n",
        "    print(x_train)\n",
        "    print(y_train )\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxHTxm2fDtHY",
        "outputId": "cf9c28ad-cebd-4597-e8bb-25becb3dae80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([0.00015211, 0.00019161, 0.00023606, 0.00016001, 0.00021729,\n",
            "       0.00022026, 0.00024693, 0.00037434, 0.0003941 , 0.00043262,\n",
            "       0.00056497, 0.00085339, 0.00091561, 0.00112304, 0.00094327,\n",
            "       0.00104501, 0.00105489, 0.00124848, 0.00103513, 0.0009976 ,\n",
            "       0.0012791 , 0.0016169 , 0.00199026, 0.00216015, 0.001931  ,\n",
            "       0.00183223, 0.00168209, 0.00197248, 0.00204162, 0.00204755,\n",
            "       0.00211965, 0.00204854, 0.00197347, 0.00151319, 0.00171469,\n",
            "       0.00170283, 0.00126428, 0.00103118, 0.001126  , 0.00121292,\n",
            "       0.0012228 , 0.00134923, 0.00139268, 0.00160406, 0.00174432,\n",
            "       0.0018905 , 0.00171864, 0.00166332, 0.00165344, 0.00165937,\n",
            "       0.00150528, 0.00171271, 0.00148948, 0.0013354 , 0.00137491,\n",
            "       0.00153294, 0.00161986, 0.00186284, 0.0018984 , 0.00153887])]\n",
            "[0.001685049873928035]\n",
            "\n",
            "[array([0.00015211, 0.00019161, 0.00023606, 0.00016001, 0.00021729,\n",
            "       0.00022026, 0.00024693, 0.00037434, 0.0003941 , 0.00043262,\n",
            "       0.00056497, 0.00085339, 0.00091561, 0.00112304, 0.00094327,\n",
            "       0.00104501, 0.00105489, 0.00124848, 0.00103513, 0.0009976 ,\n",
            "       0.0012791 , 0.0016169 , 0.00199026, 0.00216015, 0.001931  ,\n",
            "       0.00183223, 0.00168209, 0.00197248, 0.00204162, 0.00204755,\n",
            "       0.00211965, 0.00204854, 0.00197347, 0.00151319, 0.00171469,\n",
            "       0.00170283, 0.00126428, 0.00103118, 0.001126  , 0.00121292,\n",
            "       0.0012228 , 0.00134923, 0.00139268, 0.00160406, 0.00174432,\n",
            "       0.0018905 , 0.00171864, 0.00166332, 0.00165344, 0.00165937,\n",
            "       0.00150528, 0.00171271, 0.00148948, 0.0013354 , 0.00137491,\n",
            "       0.00153294, 0.00161986, 0.00186284, 0.0018984 , 0.00153887]), array([0.00019161, 0.00023606, 0.00016001, 0.00021729, 0.00022026,\n",
            "       0.00024693, 0.00037434, 0.0003941 , 0.00043262, 0.00056497,\n",
            "       0.00085339, 0.00091561, 0.00112304, 0.00094327, 0.00104501,\n",
            "       0.00105489, 0.00124848, 0.00103513, 0.0009976 , 0.0012791 ,\n",
            "       0.0016169 , 0.00199026, 0.00216015, 0.001931  , 0.00183223,\n",
            "       0.00168209, 0.00197248, 0.00204162, 0.00204755, 0.00211965,\n",
            "       0.00204854, 0.00197347, 0.00151319, 0.00171469, 0.00170283,\n",
            "       0.00126428, 0.00103118, 0.001126  , 0.00121292, 0.0012228 ,\n",
            "       0.00134923, 0.00139268, 0.00160406, 0.00174432, 0.0018905 ,\n",
            "       0.00171864, 0.00166332, 0.00165344, 0.00165937, 0.00150528,\n",
            "       0.00171271, 0.00148948, 0.0013354 , 0.00137491, 0.00153294,\n",
            "       0.00161986, 0.00186284, 0.0018984 , 0.00153887, 0.00168505])]\n",
            "[0.001685049873928035, 0.0013650283261533603]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#convert the x_train and y_train to numpy arrays\n",
        "x_train,y_train=np.array(x_train),np.array(y_train)"
      ],
      "metadata": {
        "id": "FmHWe-S_H0ge"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reshape the data\n",
        "x_train=np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n",
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7vaSs98Go6Z",
        "outputId": "bd3fba87-c1a4-427d-cbdc-23ae17d09ac6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3640, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bulit the LSTM model\n",
        "model=Sequential()\n",
        "model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
        "model.add(LSTM(50,return_sequences=False))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "metadata": {
        "id": "PcPFLrjSJrLQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#compile the model\n",
        "model.compile(optimizer='adam',loss='mean_squared_error')\n"
      ],
      "metadata": {
        "id": "HGDeeLXgMuWN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train the model\n",
        "model.fit(x_train,y_train,batch_size=1,epochs=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jiz8-3dON8rm",
        "outputId": "9779f892-23fa-4300-8ff8-e017906e68f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3640/3640 [==============================] - 121s 32ms/step - loss: 1.0901e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f0a25716d40>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create the testing datset\n",
        "#create a new array containing scaled values from index\n",
        "test_data=scaled_data[training_data_len-60:,:]\n",
        "#create the dataset x_train and y_train\n",
        "x_test=[]\n",
        "y_test=dataset[training_data_len:,:]\n",
        "for i in range(60,len(test_data)):\n",
        "  x_test.append(test_data[i-60:i,0])\n"
      ],
      "metadata": {
        "id": "nP0IB8YzS_7O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#convert the data to numpy array\n",
        "x_test=np.array(x_test)\n"
      ],
      "metadata": {
        "id": "-JP3px8TVNPK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reshape  the data\n",
        "x_test=np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))"
      ],
      "metadata": {
        "id": "GgY3ujjbVsQJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#get models predicted price values\n",
        "predictions=model.predict(x_test)\n",
        "predictions=scaler.inverse_transform(predictions)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5eiaBfbWFG3",
        "outputId": "ecf4569e-9ae2-4327-de10-b4f484c2d94a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29/29 [==============================] - 1s 17ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get root mean square error(RMSE)\n",
        "rsme=np.sqrt(np.mean(predictions-y_test)**2)\n",
        "rsme\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rmms1wp4Woto",
        "outputId": "7aa3af65-746b-40c0-afaf-b6561226ff94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.993247449140012"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "train = data[:training_data_len]\n",
        "valid = data[training_data_len:]\n",
        "valid['Predictions'] = predictions\n",
        "\n",
        "# Visualize the data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date', fontsize=16)\n",
        "plt.ylabel('Close Price USD($)', fontsize=16)\n",
        "plt.tight_layout()\n",
        "\n",
        "# Plot the training data\n",
        "plt.plot(train['Close'], label='Train')\n",
        "\n",
        "# Plot the validation data (Close prices)\n",
        "plt.plot(valid['Close'], label='Validation')\n",
        "\n",
        "# Plot the predicted data\n",
        "plt.plot(valid['Predictions'], label='Predictions')\n",
        "\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "t-4Mc9gqZe0b",
        "outputId": "06f579c2-441a-4c5c-fac3-e882a1adef9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-93-1757b731668a>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  valid['Predictions'] = predictions\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#show the valid and predicted price\n",
        "valid"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "trGAx0dydxGg",
        "outputId": "180fb93f-2918-44d0-f369-e839f0e54943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Close  Predictions\n",
              "Date                               \n",
              "2019-09-26   54.972500    54.555607\n",
              "2019-09-27   54.705002    54.563393\n",
              "2019-09-30   55.992500    54.544716\n",
              "2019-10-01   56.147499    54.674911\n",
              "2019-10-02   54.740002    54.877998\n",
              "...                ...          ...\n",
              "2023-05-22  174.199997   163.835098\n",
              "2023-05-23  171.559998   164.107086\n",
              "2023-05-24  171.839996   163.959793\n",
              "2023-05-25  172.990005   163.698502\n",
              "2023-05-26  175.429993   163.561356\n",
              "\n",
              "[924 rows x 2 columns]"
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              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
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              "      <th>Date</th>\n",
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              "      <th>2019-09-26</th>\n",
              "      <td>54.972500</td>\n",
              "      <td>54.555607</td>\n",
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              "    <tr>\n",
              "      <th>2019-09-27</th>\n",
              "      <td>54.705002</td>\n",
              "      <td>54.563393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>55.992500</td>\n",
              "      <td>54.544716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-01</th>\n",
              "      <td>56.147499</td>\n",
              "      <td>54.674911</td>\n",
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              "      <th>2019-10-02</th>\n",
              "      <td>54.740002</td>\n",
              "      <td>54.877998</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2023-05-22</th>\n",
              "      <td>174.199997</td>\n",
              "      <td>163.835098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-05-23</th>\n",
              "      <td>171.559998</td>\n",
              "      <td>164.107086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-05-24</th>\n",
              "      <td>171.839996</td>\n",
              "      <td>163.959793</td>\n",
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              "      <th>2023-05-25</th>\n",
              "      <td>172.990005</td>\n",
              "      <td>163.698502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-05-26</th>\n",
              "      <td>175.429993</td>\n",
              "      <td>163.561356</td>\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ticker=yf.Ticker('AAPL')\n",
        "apple_quote=yf.download(\"AAPL\",start='2014-01-13',end='2023-06-1',interval='1d')\n",
        "new_df=apple_quote.filter(['Close'])\n",
        "#get the last 60 days closing price value convert into datframe to an array\n",
        "last_60_days=new_df[-60:].values\n",
        "#scale the data to be values between 0 to 1\n",
        "last_60_days_scaled=scaler.transform(last_60_days)\n",
        "#create an empty list\n",
        "x_test=[]\n",
        "#Append the last 60 days\n",
        "x_test.append(last_60_days_scaled)\n",
        "#convert the x_test data set to a numpy array\n",
        "x_test=np.array(x_test)\n",
        "#Reshape the data\n",
        "x_test=np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))\n",
        "#get the predicted scaled price\n",
        "pred_price=model.predict(x_test)\n",
        "#undo the scaling\n",
        "pred_price=scaler.inverse_transform(pred_price)\n",
        "pred_price\n",
        "#predict the price for next day 2023-6-2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THgS_lxjeCi6",
        "outputId": "04a7c4e8-3ee0-4d65-ecb3-7faf1f4f0179"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "1/1 [==============================] - 0s 27ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[165.0104]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ticker=yf.Ticker('AAPL')\n",
        "apple_quote2=yf.download(\"AAPL\",start='2023-05-26',end='2023-05-31',interval='1d')\n",
        "print(apple_quote2['Close'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oKxHp87mP9r",
        "outputId": "4dad517c-f1ce-4058-d4ad-d4df5da07002"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "Date\n",
            "2023-05-26    175.429993\n",
            "2023-05-30    177.300003\n",
            "Name: Close, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install keras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qak8fojf3vgY",
        "outputId": "97467131-bfc4-4b5f-adaa-37b529ab6e70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.13.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install scikit-learn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0hn_l3s33Fc",
        "outputId": "4a514f82-b57a-4509-d04c-29ded6bc2b24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pandas-datareader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcDyJEDQ50WZ",
        "outputId": "823e8421-f356-49a0-8a88-ba24b6665f7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.10/dist-packages (0.10.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (4.9.3)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (2.31.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2023.3.post1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (1.23.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (2023.7.22)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas-datareader) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ts6t_dj-MhwO",
        "outputId": "f49a296d-7f75-4a57-da81-444482ce714d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.13.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.1.21 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26)\n",
            "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.57.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.9.0)\n",
            "Requirement already satisfied: keras<2.14,>=2.13.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.13.1)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (16.0.6)\n",
            "Requirement already satisfied: numpy<=1.24.3,>=1.22 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.23.5)\n",
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          ]
        }
      ]
    }
  ]
}