cohorts/2023/01-intro/homework.ipynb

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      "metadata": {
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q1. Downloading the data\n",
        "\n"
      ],
      "metadata": {
        "id": "97yfKrCqBVSc"
      },
      "id": "97yfKrCqBVSc"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "cbe379c6-5a79-491b-af10-c85039a1d13c",
      "metadata": {
        "id": "cbe379c6-5a79-491b-af10-c85039a1d13c"
      },
      "outputs": [],
      "source": [
        "df = pd.read_parquet('data/yellow_tripdata_2022-01.parquet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "6635ef08-4a6d-43f4-9b36-26c728aec8be",
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          "height": 270
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
              "0         1  2022-01-01 00:35:40   2022-01-01 00:53:29              2.0   \n",
              "1         1  2022-01-01 00:33:43   2022-01-01 00:42:07              1.0   \n",
              "2         2  2022-01-01 00:53:21   2022-01-01 01:02:19              1.0   \n",
              "3         2  2022-01-01 00:25:21   2022-01-01 00:35:23              1.0   \n",
              "4         2  2022-01-01 00:36:48   2022-01-01 01:14:20              1.0   \n",
              "\n",
              "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
              "0           3.80         1.0                  N           142           236   \n",
              "1           2.10         1.0                  N           236            42   \n",
              "2           0.97         1.0                  N           166           166   \n",
              "3           1.09         1.0                  N           114            68   \n",
              "4           4.30         1.0                  N            68           163   \n",
              "\n",
              "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
              "0             1         14.5    3.0      0.5        3.65           0.0   \n",
              "1             1          8.0    0.5      0.5        4.00           0.0   \n",
              "2             1          7.5    0.5      0.5        1.76           0.0   \n",
              "3             2          8.0    0.5      0.5        0.00           0.0   \n",
              "4             1         23.5    0.5      0.5        3.00           0.0   \n",
              "\n",
              "   improvement_surcharge  total_amount  congestion_surcharge  airport_fee  \n",
              "0                    0.3         21.95                   2.5          0.0  \n",
              "1                    0.3         13.30                   0.0          0.0  \n",
              "2                    0.3         10.56                   0.0          0.0  \n",
              "3                    0.3         11.80                   2.5          0.0  \n",
              "4                    0.3         30.30                   2.5          0.0  "
            ],
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              "\n",
              "  <div id=\"df-5e4f3553-55bb-47b1-a529-2aee25e3dda1\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "      <th>congestion_surcharge</th>\n",
              "      <th>airport_fee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2022-01-01 00:35:40</td>\n",
              "      <td>2022-01-01 00:53:29</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.80</td>\n",
              "      <td>1.0</td>\n",
              "      <td>N</td>\n",
              "      <td>142</td>\n",
              "      <td>236</td>\n",
              "      <td>1</td>\n",
              "      <td>14.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3.65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>21.95</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2022-01-01 00:33:43</td>\n",
              "      <td>2022-01-01 00:42:07</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>N</td>\n",
              "      <td>236</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>13.30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2022-01-01 00:53:21</td>\n",
              "      <td>2022-01-01 01:02:19</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.97</td>\n",
              "      <td>1.0</td>\n",
              "      <td>N</td>\n",
              "      <td>166</td>\n",
              "      <td>166</td>\n",
              "      <td>1</td>\n",
              "      <td>7.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.76</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>10.56</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>2022-01-01 00:25:21</td>\n",
              "      <td>2022-01-01 00:35:23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.09</td>\n",
              "      <td>1.0</td>\n",
              "      <td>N</td>\n",
              "      <td>114</td>\n",
              "      <td>68</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>11.80</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>2022-01-01 00:36:48</td>\n",
              "      <td>2022-01-01 01:14:20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>N</td>\n",
              "      <td>68</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>23.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>30.30</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-5e4f3553-55bb-47b1-a529-2aee25e3dda1');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "f055344d-f050-4fcf-8453-ee4a66407bfe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f055344d-f050-4fcf-8453-ee4a66407bfe",
        "outputId": "68b659fe-15ba-468b-9f87-41122d66ba85"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "len(df.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Q2. Computing duration"
      ],
      "metadata": {
        "id": "pWrLGUESBed6"
      },
      "id": "pWrLGUESBed6"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "b186962d-7ee1-4b97-a457-7ef2b2de811a",
      "metadata": {
        "id": "b186962d-7ee1-4b97-a457-7ef2b2de811a"
      },
      "outputs": [],
      "source": [
        "df['duration'] = df.tpep_dropoff_datetime - df.tpep_pickup_datetime\n",
        "df['duration'] = df.duration.dt.total_seconds() / 60"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "0e3e89da-45a3-4b12-99bb-8b56457cfa5a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0e3e89da-45a3-4b12-99bb-8b56457cfa5a",
        "outputId": "289c84a9-b93f-4400-edd9-300dada8dec0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.212202918831741"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.duration.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "9da88772-b29c-4c46-8859-7c28ac2be021",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9da88772-b29c-4c46-8859-7c28ac2be021",
        "outputId": "d8bc9a61-e953-400a-98a5-9613b8bc7021"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46.44530513776802"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.duration.std()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ca0c395a-d5cf-4870-aa64-edb4809bc662",
      "metadata": {
        "id": "ca0c395a-d5cf-4870-aa64-edb4809bc662"
      },
      "source": [
        "### Q3. Dropping outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "a06d9882-82bc-40eb-954e-ebabde6e5431",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a06d9882-82bc-40eb-954e-ebabde6e5431",
        "outputId": "b318ae8f-3d3c-4115-8111-9415bd7def9b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98.27547930522405"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "len(df[(df.duration >= 1) & (df.duration <= 60)]) / len(df) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "0fd4307e-f3b6-45ba-bf1d-82e4a1c09a9b",
      "metadata": {
        "id": "0fd4307e-f3b6-45ba-bf1d-82e4a1c09a9b"
      },
      "outputs": [],
      "source": [
        "df = df[(df.duration >= 1) & (df.duration <= 60)].copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00b428a6-3438-4482-91fb-04ed17f5fe8d",
      "metadata": {
        "id": "00b428a6-3438-4482-91fb-04ed17f5fe8d"
      },
      "source": [
        "### Q4. One-hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "96043224-1a51-4922-901c-c660b0bc8a5b",
      "metadata": {
        "id": "96043224-1a51-4922-901c-c660b0bc8a5b"
      },
      "outputs": [],
      "source": [
        "categorical = ['PULocationID', 'DOLocationID']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "8853a0e2-7d3f-46e6-a45d-eeadc7c65bd1",
      "metadata": {
        "id": "8853a0e2-7d3f-46e6-a45d-eeadc7c65bd1"
      },
      "outputs": [],
      "source": [
        "df[categorical] = df[categorical].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "3bb3cef4-bb13-4b20-a1ba-0c23b27062de",
      "metadata": {
        "id": "3bb3cef4-bb13-4b20-a1ba-0c23b27062de"
      },
      "outputs": [],
      "source": [
        "train_dicts = df[categorical].to_dict(orient='records')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "f89a6a92-bc5b-4aac-9366-aa9ea7029df6",
      "metadata": {
        "id": "f89a6a92-bc5b-4aac-9366-aa9ea7029df6"
      },
      "outputs": [],
      "source": [
        "dv = DictVectorizer()\n",
        "X_train = dv.fit_transform(train_dicts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "bfb1d21f-23e8-411d-b70d-748b9b672efb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfb1d21f-23e8-411d-b70d-748b9b672efb",
        "outputId": "46aafa55-2e06-4f5b-b0cf-a14353d3dd95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature matrix size: (2421440, 515)\n"
          ]
        }
      ],
      "source": [
        "print(f'Feature matrix size: {X_train.shape}')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bb9c2670-07b9-4813-9798-d367fc6535fe",
      "metadata": {
        "id": "bb9c2670-07b9-4813-9798-d367fc6535fe"
      },
      "source": [
        "### Q5. Training a model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "098c5ff8-8e41-41a6-a7cd-760f9c79c1f9",
      "metadata": {
        "id": "098c5ff8-8e41-41a6-a7cd-760f9c79c1f9"
      },
      "outputs": [],
      "source": [
        "target = 'duration'\n",
        "y_train = df[target].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "986fd495-62f8-455c-a652-946f144c5832",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "986fd495-62f8-455c-a652-946f144c5832",
        "outputId": "f71a2119-1f4f-4d05-d60c-ad6dbd72b6c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train RMSE: 6.986190742248472\n"
          ]
        }
      ],
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)\n",
        "\n",
        "y_pred = lr.predict(X_train)\n",
        "\n",
        "print(f'Train RMSE: {mean_squared_error(y_train, y_pred, squared=False)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "87051e49-1293-4e56-bda8-38e4f206e0ed",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "87051e49-1293-4e56-bda8-38e4f206e0ed",
        "outputId": "fb12601f-650e-421b-e120-a8a3236f37aa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(y_pred, label='prediction')\n",
        "sns.histplot(y_train, label='actual')\n",
        "\n",
        "plt.legend();"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5259854b-65a5-4fd0-a8b9-87a41ce4835d",
      "metadata": {
        "id": "5259854b-65a5-4fd0-a8b9-87a41ce4835d"
      },
      "source": [
        "### Q6. Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "86965e06-89c1-47eb-bc1a-734721e389cd",
      "metadata": {
        "id": "86965e06-89c1-47eb-bc1a-734721e389cd"
      },
      "outputs": [],
      "source": [
        "categorical = ['PULocationID', 'DOLocationID']\n",
        "\n",
        "def read_data(filename):\n",
        "    df = pd.read_parquet(filename)\n",
        "\n",
        "    df['duration'] = df.tpep_dropoff_datetime - df.tpep_pickup_datetime\n",
        "    df.duration = df.duration.dt.total_seconds() / 60\n",
        "\n",
        "    df = df[(df.duration >= 1) & (df.duration <= 60)].copy()\n",
        "\n",
        "    df[categorical] = df[categorical].astype('str')\n",
        "    \n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "3e3c42e7-4fc2-4478-aa33-3b70bc12597a",
      "metadata": {
        "id": "3e3c42e7-4fc2-4478-aa33-3b70bc12597a"
      },
      "outputs": [],
      "source": [
        "df_val = read_data('data/yellow_tripdata_2022-02.parquet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "c3fd052d-029c-4358-8672-81bc50561832",
      "metadata": {
        "id": "c3fd052d-029c-4358-8672-81bc50561832"
      },
      "outputs": [],
      "source": [
        "val_dicts = df_val[categorical].to_dict(orient='records')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "2ccad0a0-8e05-48e3-963b-5d91e8092867",
      "metadata": {
        "id": "2ccad0a0-8e05-48e3-963b-5d91e8092867"
      },
      "outputs": [],
      "source": [
        "X_val = dv.transform(val_dicts) \n",
        "y_val = df_val.duration.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "4a87e14b-92f8-4fbc-8fa9-77c1afe1ca56",
      "metadata": {
        "id": "4a87e14b-92f8-4fbc-8fa9-77c1afe1ca56"
      },
      "outputs": [],
      "source": [
        "y_pred = lr.predict(X_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "994d39c8-199d-462f-b249-b41aabc12cc1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "994d39c8-199d-462f-b249-b41aabc12cc1",
        "outputId": "b8625583-5828-4552-901a-cd4e38f9d2d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Val RMSE: 7.78640662117552\n"
          ]
        }
      ],
      "source": [
        "print(f'Val RMSE: {mean_squared_error(y_val, y_pred, squared=False)}')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.0"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}