Student_MLE_MiniProject_EDA.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/brettdavies/mec2-projects/blob/main/Student_MLE_MiniProject_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Mini Project: Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "hVq3RjKJpJI0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis: Unveiling Insights from the NYC Taxi Dataset\n",
        "\n",
        "Data has become the lifeblood of the modern world, permeating every aspect of our lives and transforming the way we make decisions. In this era of vast information, the ability to extract meaningful insights from raw data has emerged as a crucial skill. Enter exploratory data analysis (EDA), a powerful approach that allows us to unravel hidden patterns, detect anomalies, and generate valuable knowledge from the vast volumes of data at our disposal.\n",
        "\n",
        "Exploratory data analysis serves as the initial step in any data-driven investigation, offering a comprehensive understanding of the dataset's structure, distributions, and relationships between variables. By applying statistical and visual techniques, analysts gain a deeper insight into the data, paving the way for more accurate predictions, informed decision-making, and the discovery of actionable insights.\n",
        "\n",
        "To illustrate the significance of exploratory data analysis, we delve into one of the most popular and widely studied datasets in the field—the [NYC Taxi Dataset](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page). As the name suggests, this dataset captures detailed information about taxi trips within the bustling city of New York. The NYC Taxi Dataset is an ideal choice for learning and practicing EDA techniques due to its richness, complexity, and real-world applicability.\n",
        "\n",
        "The dataset encompasses a vast range of attributes, including pickup and drop-off locations, timestamps, trip durations, passenger counts, payment information, and much more. By exploring this data, we can gain valuable insights into the dynamics of taxi usage, understand travel patterns across different neighborhoods, identify peak hours of demand, analyze fare structures, and even uncover interesting anecdotes about the city's vibrant life.\n",
        "\n",
        "The NYC Taxi Dataset is an excellent resource for aspiring ML practitioners to develop their EDA skills. Its scale, complexity, and real-world relevance make it an engaging playground for uncovering hidden patterns, generating hypotheses, and forming data-driven narratives.\n",
        "\n",
        "In this mini project, we will dive deep into the NYC Taxi Dataset. We will leverage various EDA techniques to unveil meaningful insights, visualize data distributions, identify outliers, and pose insightful questions that will fuel further analysis and exploration. By the end of this colab, students will have a solid foundation in exploratory data analysis and be equipped to tackle real-world data challenges with confidence."
      ],
      "metadata": {
        "id": "ajb94WgIRdgC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "lbJFWLELlI6N"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the NYC taxi dataset into a Pandas DataFrame and do a few basic checks to ensure the data is loaded properly. Note, there are several months of data that can be used. For simplicity, use the Yellow Taxi 2022-01 parquet file [here](https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet). Here are your tasks:\n",
        "\n",
        "  1. Load the `yellow_tripdata_2022-01.parquet` file into Pandas.\n",
        "  2. Print the first 5 rows of data. Study the schema and make sure you understand what each of the fields mean by referencing the [documentation](https://www.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf).\n",
        "  3. How many rows are in the dataset? How many unique columns are in the dataset?\n",
        "  4. Which columns have NULL values and how many NULL values are present in each of these columns?\n",
        "  5. Generate summary statistics using Pandas' [describe method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html). Do you notice anything unusual in the dataset? Find at least one anomoly and try to come up with a hypothesis to explain it.  \n",
        "  6. Drop all rows with NULL values and store the result. We'll ignore NULL valued rows in this mini-project.\n"
      ],
      "metadata": {
        "id": "sgK6-XtjVnjj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load parquet file into a Pandas DataFrame\n",
        "df = pd.read_parquet(\"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet\")"
      ],
      "metadata": {
        "id": "db--eb8zlNNg"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset\n",
        "print(df.head())"
      ],
      "metadata": {
        "id": "TslBuHoXl_o1",
        "outputId": "686823fb-7115-4da0-fab9-feaab90ca51a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   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  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the shape of the dataset\n",
        "print(df.shape)"
      ],
      "metadata": {
        "id": "jkQN2lBymKH-",
        "outputId": "d9130c24-fc51-4eba-87e7-adc9bed5d22f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2463931, 19)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df.isnull().any())"
      ],
      "metadata": {
        "id": "N8JeOtV5mQJ7",
        "outputId": "5b753651-6295-4cf3-9c2a-1990ecf78c28",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VendorID                 False\n",
            "tpep_pickup_datetime     False\n",
            "tpep_dropoff_datetime    False\n",
            "passenger_count           True\n",
            "trip_distance            False\n",
            "RatecodeID                True\n",
            "store_and_fwd_flag        True\n",
            "PULocationID             False\n",
            "DOLocationID             False\n",
            "payment_type             False\n",
            "fare_amount              False\n",
            "extra                    False\n",
            "mta_tax                  False\n",
            "tip_amount               False\n",
            "tolls_amount             False\n",
            "improvement_surcharge    False\n",
            "total_amount             False\n",
            "congestion_surcharge      True\n",
            "airport_fee               True\n",
            "dtype: bool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary statistics of the dataset\n",
        "print(df.describe())"
      ],
      "metadata": {
        "id": "h9q5SOrql5mS",
        "outputId": "86209514-7428-4e09-dbf8-11dc4e407625",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           VendorID        tpep_pickup_datetime       tpep_dropoff_datetime  \\\n",
            "count  2.463931e+06                     2463931                     2463931   \n",
            "mean   1.707819e+00  2022-01-17 01:19:51.689724  2022-01-17 01:34:04.421902   \n",
            "min    1.000000e+00         2008-12-31 22:23:09         2008-12-31 23:06:56   \n",
            "25%    1.000000e+00         2022-01-09 15:37:41  2022-01-09 15:50:50.500000   \n",
            "50%    2.000000e+00         2022-01-17 12:11:45         2022-01-17 12:23:49   \n",
            "75%    2.000000e+00  2022-01-24 13:49:37.500000         2022-01-24 14:02:51   \n",
            "max    6.000000e+00         2022-05-18 20:41:57         2022-05-18 20:47:45   \n",
            "std    5.021375e-01                         NaN                         NaN   \n",
            "\n",
            "       passenger_count  trip_distance    RatecodeID  PULocationID  \\\n",
            "count     2.392428e+06   2.463931e+06  2.392428e+06  2.463931e+06   \n",
            "mean      1.389453e+00   5.372751e+00  1.415507e+00  1.660768e+02   \n",
            "min       0.000000e+00   0.000000e+00  1.000000e+00  1.000000e+00   \n",
            "25%       1.000000e+00   1.040000e+00  1.000000e+00  1.320000e+02   \n",
            "50%       1.000000e+00   1.740000e+00  1.000000e+00  1.620000e+02   \n",
            "75%       1.000000e+00   3.130000e+00  1.000000e+00  2.340000e+02   \n",
            "max       9.000000e+00   3.061593e+05  9.900000e+01  2.650000e+02   \n",
            "std       9.829686e-01   5.478714e+02  5.917573e+00  6.546806e+01   \n",
            "\n",
            "       DOLocationID  payment_type   fare_amount         extra       mta_tax  \\\n",
            "count  2.463931e+06  2.463931e+06  2.463931e+06  2.463931e+06  2.463931e+06   \n",
            "mean   1.635807e+02  1.194449e+00  1.294648e+01  1.006710e+00  4.914539e-01   \n",
            "min    1.000000e+00  0.000000e+00 -4.800000e+02 -4.500000e+00 -5.000000e-01   \n",
            "25%    1.130000e+02  1.000000e+00  6.500000e+00  0.000000e+00  5.000000e-01   \n",
            "50%    1.620000e+02  1.000000e+00  9.000000e+00  5.000000e-01  5.000000e-01   \n",
            "75%    2.360000e+02  1.000000e+00  1.400000e+01  2.500000e+00  5.000000e-01   \n",
            "max    2.650000e+02  5.000000e+00  4.010923e+05  3.350000e+01  1.659000e+01   \n",
            "std    7.079016e+01  5.001778e-01  2.558149e+02  1.236226e+00  8.291560e-02   \n",
            "\n",
            "         tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
            "count  2.463931e+06  2.463931e+06           2.463931e+06  2.463931e+06   \n",
            "mean   2.385696e+00  3.749773e-01           2.967234e-01  1.916937e+01   \n",
            "min   -1.252200e+02 -3.140000e+01          -3.000000e-01 -4.803000e+02   \n",
            "25%    7.200000e-01  0.000000e+00           3.000000e-01  1.130000e+01   \n",
            "50%    2.000000e+00  0.000000e+00           3.000000e-01  1.469000e+01   \n",
            "75%    3.000000e+00  0.000000e+00           3.000000e-01  2.002000e+01   \n",
            "max    8.888800e+02  1.933000e+02           3.000000e-01  4.010956e+05   \n",
            "std    2.830698e+00  1.680538e+00           4.374741e-02  2.559641e+02   \n",
            "\n",
            "       congestion_surcharge   airport_fee  \n",
            "count          2.392428e+06  2.392428e+06  \n",
            "mean           2.282322e+00  8.249935e-02  \n",
            "min           -2.500000e+00 -1.250000e+00  \n",
            "25%            2.500000e+00  0.000000e+00  \n",
            "50%            2.500000e+00  0.000000e+00  \n",
            "75%            2.500000e+00  0.000000e+00  \n",
            "max            2.500000e+00  1.250000e+00  \n",
            "std            7.432040e-01  3.125554e-01  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with missing values.\n",
        "df = df.dropna(how='any')"
      ],
      "metadata": {
        "id": "VP3EWMIEo4sp"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature engineering is a critical process in machine learning that involves selecting, transforming, and creating features from raw data to improve the performance and accuracy of predictive models. While machine learning algorithms have the capability to automatically learn patterns from data, the quality and relevance of the features used as inputs greatly influence the model's ability to generalize and make accurate predictions. Feature engineering, therefore, plays a crucial role in extracting meaningful information and representing it in a format that best captures the underlying relationships within the data.\n",
        "\n",
        "Here are your tasks:\n",
        "\n",
        "  1. Create a new feature that calculates the trip duration in minutes.\n",
        "  2. Create additional features for the pick-up day of week and pick-up hour.\n",
        "  3. Use the Seaborn library to create a [line plot](https://seaborn.pydata.org/generated/seaborn.lineplot.html) depicting the number of trips as a function of the hour of day. What's the busiest time of day?\n",
        "  4. Create another lineplot depicting the number of trips as a function of the day of week. What day of the week is the least busy?\n",
        "\n",
        "A. The busiest time of day is the afternoon. The least busy day is Tuesday."
      ],
      "metadata": {
        "id": "u2i5l3QNFAf3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new column for trip duration in minutes\n",
        "df.loc[:,[\"trip_duration\"]] = df['tpep_dropoff_datetime'] - df['tpep_pickup_datetime']\n",
        "# df.info()\n",
        "# print(df['trip_duration'].head())"
      ],
      "metadata": {
        "id": "Wef2rR1Npl8f"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create new columns for pickup hour and day of week\n",
        "# df.info()\n",
        "# print(df['tpep_pickup_datetime'])\n",
        "df.loc[:,[\"tpep_pickup_hour\"]] = df['tpep_pickup_datetime'].dt.hour\n",
        "df.loc[:,[\"tpep_pickup_dow\"]] = df['tpep_pickup_datetime'].dt.dayofweek\n",
        "\n",
        "# print(df['tpep_pickup_hour'])\n",
        "# df.info()"
      ],
      "metadata": {
        "id": "d13PYaN2FPFt"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a lineplot displaying the number of trips by pickup hour\n",
        "data = df.groupby(['tpep_pickup_hour']).size()\n",
        "sns.lineplot(x=data.index, y=data.values)"
      ],
      "metadata": {
        "id": "-Bf7bnS9uU-h",
        "outputId": "f6517a54-61ba-4595-bb98-e034177f0670",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='tpep_pickup_hour'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a lineplot displaying the number of trips by pickup day\n",
        "data = df.groupby(['tpep_pickup_dow']).size()\n",
        "sns.lineplot(x=data.index,y=data.values)"
      ],
      "metadata": {
        "id": "iGjHC9lHuO8r",
        "outputId": "540753f7-9f4c-4278-a680-66e90793770a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='tpep_pickup_dow'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the realm of machine learning, understanding the relationships between variables is crucial for building accurate and effective predictive models. One powerful tool for exploring these relationships is the correlation matrix. A correlation matrix provides a comprehensive overview of the pairwise correlations between variables in a dataset, allowing practitioners to quantify and visualize the strength and direction of these associations. This matrix is an essential component of exploratory data analysis and offers several key benefits:\n",
        "\n",
        "1. Relationship Assessment: The correlation matrix provides a quantitative measure of the relationship between variables. By calculating correlation coefficients, typically using methods like Pearson's correlation coefficient, analysts can determine if variables are positively correlated (increase together), negatively correlated (one increases as the other decreases), or uncorrelated (no systematic relationship). These measures offer insights into the direction and strength of the relationships, helping to identify important variables that may influence the target variable.\n",
        "\n",
        "2. Feature Selection: Correlation matrices are invaluable in feature selection, which involves identifying the most relevant variables for building predictive models. By examining the correlations between the target variable and other features, analysts can identify highly correlated variables that may be strong predictors. This knowledge enables informed decisions regarding which variables to include in the model, potentially reducing dimensionality, enhancing model efficiency, and preventing issues such as multicollinearity.\n",
        "\n",
        "3. Multicollinearity Detection: Multicollinearity occurs when two or more independent variables in a model are highly correlated. This can lead to problems such as instability in coefficient estimates, difficulty in interpreting feature importance, and reduced model robustness. By examining the correlation matrix, analysts can identify highly correlated variables and make informed decisions about which ones to include or exclude to mitigate multicollinearity. Removing redundant variables improves model interpretability and generalization.\n",
        "\n",
        "Here is your task:\n",
        "\n",
        "  1. Compute a correlation matrix between the variables 'trip_distance', 'fare_amount', 'tip_amount', 'total_amount', 'trip_duration' and use Seaborn to create a heatmap of the results. Which variables are strongly correlated?\n",
        "\n",
        "A. 'total_amount' and 'fare_amount' are strongly correlated."
      ],
      "metadata": {
        "id": "yVQLUFXPGe4e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute correlation matrix of numerical variables\n",
        "# Compute the correlation matrix\n",
        "\n",
        "corr = df[[\"trip_distance\", \"fare_amount\", \"tip_amount\", \"total_amount\", \"trip_duration\"]].corr()\n",
        "\n",
        "# Create a heatmap of the correlation matrix\n",
        "import numpy as np\n",
        "# Generate a mask for the upper triangle\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "metadata": {
        "id": "XCY8MrLAppQz",
        "outputId": "0223e493-756c-4748-849e-66cff81257d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pairplots, also known as scatterplot matrices, allow for the visualization of pairwise relationships between multiple variables simultaneously. Each subplot in the pairplot represents the scatterplot of one variable against another. Pairplots offer several advantages in EDA:\n",
        "\n",
        "  - Variable Relationships: Pairplots enable analysts to explore the relationships between variables, revealing patterns such as linear or nonlinear correlations, clusters, or other associations. These visual cues guide further analysis, feature selection, or modeling decisions.\n",
        "\n",
        "  - Multivariate Analysis: Pairplots help identify multivariate dependencies and interactions, highlighting how different variables jointly influence one another. This is particularly valuable in identifying potential confounding factors or discovering hidden interactions that may not be apparent when considering variables in isolation.\n",
        "\n",
        "  - Outlier Detection: Pairplots can reveal potential outliers by showing data points that deviate significantly from the general pattern observed between variables. Outliers can indicate data anomalies or influential observations that may impact model performance.\n",
        "\n",
        "  - Feature Importance: Pairplots provide an intuitive representation of the relative importance of different features. Variables exhibiting strong correlations or clear patterns may be more relevant for predictive modeling or feature selection.\n",
        "\n",
        "  - Data Quality: Pairplots can help identify data quality issues, such as data entry errors or measurement inconsistencies. Patterns that do not align with expectations or exhibit unusual trends may signal data problems that require further investigation or preprocessing.\n",
        "\n",
        "Here is your task:\n",
        "\n",
        "  1. Create a [pairplot matrix](https://seaborn.pydata.org/generated/seaborn.pairplot.html) using Seaborn to observation the relationship between the following variables: trip_distance, fare_amount, tip_amount, total_amount, trip_duration. Note, pairplots can be memory intensive. Try sampling the dataset using the [sample method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sample.html) in Pandas. Which variables appear to have a strong relationship? Which variables seem to have no relationship?\n",
        "\n",
        "A. 'trip_distance', 'fare_amount', 'tip_amount', and 'total_amount' all seem to have a strong relationship with eachother. 'trip_duration' does not seem to have a strong relationship with any of the other variables."
      ],
      "metadata": {
        "id": "Eh6TJ8iRJHDm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot matrix of numerical variables. If memory issues try the df.sample method.\n",
        "sns.pairplot(\n",
        "    df[[\"trip_distance\", \"fare_amount\", \"tip_amount\", \"total_amount\", \"trip_duration\"]].sample(frac=0.01, replace=False, random_state=1)\n",
        "    )"
      ],
      "metadata": {
        "id": "l_v24Ym3p8A-",
        "outputId": "b05d7467-4086-4c47-9eb7-198814e800fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fb8642920b0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A count plot is a type of categorical plot that displays the number of occurrences of each category in a dataset. It is particularly useful for visualizing the distribution and frequency of categorical variables. Here are some key uses and benefits of count plots:\n",
        "\n",
        "  - Categorical Variable Exploration: Count plots provide a quick and concise summary of the distribution of categorical variables. They allow analysts to understand the frequency or count of each category, revealing the proportions and imbalances within the dataset. This information is crucial for gaining insights into the composition and characteristics of categorical variables.\n",
        "\n",
        "  - Class Imbalance Assessment: In classification tasks, count plots help assess the balance or imbalance of different target classes. It provides a visual representation of the distribution of classes, highlighting any significant discrepancies in the sample sizes across categories. Identifying imbalanced classes is important in machine learning as it can affect model performance and bias the predictions towards the majority class.\n",
        "\n",
        "  - Data Quality Inspection: Count plots can be utilized to detect data quality issues in categorical variables. It allows analysts to identify unexpected or erroneous categories that may indicate data entry errors, missing data, or inconsistencies in the dataset. By observing the counts for each category, anomalies or discrepancies can be easily spotted, enabling data cleaning or further investigation if necessary.\n",
        "\n",
        "  - Feature Importance Evaluation: Count plots can provide insights into the importance or relevance of different categorical features in relation to the target variable. By visualizing the distribution of categories within each class or target level, analysts can determine which categories are more prevalent or have higher frequencies for specific outcomes. This understanding helps in assessing the discriminatory power of categorical features and their potential impact on predictive models.\n",
        "\n",
        "Here is your task:\n",
        "\n",
        "  1. Use Seaborn to create a [countplot](https://seaborn.pydata.org/generated/seaborn.countplot.html) for the variables PULocationID, and DOLocationID. Keep only the top 15 pick-up and drop-off locations. What's the most popular pick-up location?\n",
        "\n",
        "A. The most popular pick-up location is PULocationID 237."
      ],
      "metadata": {
        "id": "OA1p83hesFkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Seaborn countplot for PULocationID and DOLocationID. Only plot the top 15 categories by value counts.\n",
        "# Count Plot for PULocationID\n",
        "print (\"Count Plot for fare_amount\")\n",
        "pu_loc_cnt = df['PULocationID'].value_counts().head(15)\n",
        "# print(pu_loc_cnt)\n",
        "sns.countplot(data=df, x=\"PULocationID\", order=pu_loc_cnt.index)\n",
        "plt.show()\n",
        "\n",
        "# Count Plot for DOLocationID\n",
        "print (\"Count Plot for DOLocationID\")\n",
        "do_loc_cnt = df['DOLocationID'].value_counts().head(15)\n",
        "# print(pu_loc_cnt)\n",
        "sns.countplot(data=df, x=\"DOLocationID\", order=do_loc_cnt.index)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "eigDjtkTruQD",
        "outputId": "a8518cbd-5ea9-4081-cb79-d5f9b52f9e71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count Plot for fare_amount\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count Plot for DOLocationID\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A box plot, also known as a box-and-whisker plot, is a powerful visualization tool for displaying the distribution, variability, and outliers within a numerical dataset. It provides a concise summary of key statistical measures and offers several important uses:\n",
        "\n",
        "  - Data Distribution and Skewness: Box plots offer a visual representation of the distribution of numerical data, providing insights into its central tendency, spread, and skewness. The box represents the interquartile range (IQR), which contains the middle 50% of the data, with the median indicated by a horizontal line within the box. By observing the length and symmetry of the box, analysts can assess whether the data is skewed or symmetrically distributed.\n",
        "\n",
        "  - Outlier Detection: Box plots are highly effective in identifying outliers, which are data points that deviate significantly from the rest of the distribution. The whiskers of the plot extend to the minimum and maximum non-outlier values, with any data points beyond the whiskers considered as potential outliers. Outliers can indicate data errors, anomalies, or important observations requiring further investigation.\n",
        "\n",
        "  - Comparing Groups or Categories: Box plots are useful for comparing the distributions of numerical data across different groups or categories. By creating side-by-side or grouped box plots, analysts can easily compare the central tendencies, spreads, and shapes of distributions between different groups. This allows for the identification of differences, similarities, or patterns within the data.\n",
        "\n",
        "  - Variability and Spread: Box plots provide insights into the variability and spread of the data. The length of the box indicates the spread of the middle 50% of the data, while the whiskers show the range of non-outlier values. By comparing the lengths of the boxes and whiskers, analysts can assess the relative variability between different groups or categories, aiding in the understanding of the data's dispersion.\n",
        "\n",
        "  - Skewedness and Symmetry: Box plots offer a visual assessment of the skewness or symmetry of the data distribution. A symmetrical distribution is represented by a box plot with an equal length on both sides of the median, while a skewed distribution is indicated by a longer box on one side. This visual cue helps in understanding the shape and characteristics of the data, assisting in further analysis and modeling decisions.\n",
        "\n",
        "  - Data Range and Quartiles: Box plots display the quartiles of the data distribution. The lower quartile (Q1) represents the 25th percentile, the upper quartile (Q3) represents the 75th percentile, and the interquartile range (IQR) is the range between Q1 and Q3. These quartiles provide a summary of the range and spread of the central portion of the data, aiding in the understanding of the data's variability and dispersion.\n",
        "\n",
        "Your task is:\n",
        "\n",
        "  1. Use Seaborn's [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) to discern the relationship between payment_type and total_amount. Does anything look weird? Can you explain what's going on?\n",
        "\n",
        "A. Yes, there is something that looks weird. There is an extreme outlier within payment_type = 4."
      ],
      "metadata": {
        "id": "Bbl-WZtctVbH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "fM_2IGKrk8Vy",
        "outputId": "79d3901b-1e7f-4576-b09b-f51ff022d89c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='payment_type', ylabel='total_amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a box plot of total amount by payment type. Do you see anything odd?\n",
        "sns.boxplot(data=df, x=\"payment_type\", y=\"total_amount\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A histogram is a graphical representation that displays the distribution of a continuous or discrete numerical variable. It provides insights into the underlying data distribution and helps uncover patterns, frequencies, and ranges within the dataset. Here are some key uses and benefits of histogram plots:\n",
        "\n",
        "  - Data Distribution: Histograms allow analysts to visualize the shape, central tendency, and spread of the data. They provide an overview of the data distribution, helping to identify if it follows a particular pattern, such as a normal distribution, skewed distribution, bimodal distribution, or multimodal distribution. Understanding the data distribution aids in selecting appropriate analysis techniques and understanding the characteristics of the data.\n",
        "\n",
        "  - Frequency Analysis: Histograms display the frequency or count of data points within predefined bins or intervals along the x-axis. By observing the height or count of each bin, analysts can identify the frequency of occurrence for different values or value ranges. This information helps assess the concentration of data points and identify peaks or modes in the distribution.\n",
        "\n",
        "  - Outlier Detection: Histograms can assist in identifying outliers, which are data points that significantly deviate from the rest of the distribution. Outliers may indicate data errors, unusual observations, or important anomalies requiring further investigation. By examining the tails or extreme values in the histogram, analysts can identify potential outliers that may require additional scrutiny.\n",
        "\n",
        "  - Data Range and Spread: Histograms provide insights into the range and spread of the data. The x-axis represents the variable's values, while the y-axis represents the frequency or count of occurrences. By observing the width and span of the histogram, analysts can assess the data's range and variability. This information helps understand the data's spread and aids in subsequent analysis or decision-making processes.\n",
        "\n",
        "  - Feature Engineering: Histograms can guide feature engineering processes by informing appropriate transformations, binning strategies, or encoding techniques for numerical variables. They assist in identifying nonlinear relationships or determining optimal cut-off points for converting continuous variables into categorical ones. Histograms also help identify data skewness and guide transformation methods to address the skew if necessary.\n",
        "\n",
        "  - Data Quality Inspection: Histograms can be useful in detecting data quality issues or anomalies. Unusual spikes, gaps, or unexpected patterns in the histogram may indicate data entry errors, measurement inconsistencies, or missing data. By observing the histogram, analysts can identify potential data quality issues that require further investigation or preprocessing.\n",
        "\n",
        "Your task is:\n",
        "\n",
        "  1. Use Seaborn's [histplot](https://seaborn.pydata.org/generated/seaborn.histplot.html) to explore the data distributions for fare_amount, trip_distance, and extra. Use kernel density estimators to better visualize the distribution. Use sampling if you run into any memory issues."
      ],
      "metadata": {
        "id": "CnV0dIHquPtf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore data distributions for 'fare_amount', 'trip_distance' and 'extra' using Seaborn's histplot. Sample the data if you run into memory issues.\n",
        "# Hist Plot for fare_amount\n",
        "print (\"Hist Plot for fare_amount\")\n",
        "sns.histplot(data=df.sample(frac=0.01, replace=False, random_state=1), x='fare_amount', kde=True)\n",
        "plt.show()\n",
        "\n",
        "# Hist Plot for trip_distance\n",
        "print (\"Hist Plot for trip_distance\")\n",
        "sns.histplot(data=df.sample(frac=0.01, replace=False, random_state=1), x='trip_distance', kde=True)\n",
        "plt.show()\n",
        "\n",
        "# Hist Plot for extra\n",
        "print (\"Hist Plot for extra\")\n",
        "sns.histplot(data=df.sample(frac=0.01, replace=False, random_state=1), x='extra', kde=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "8HI3uXHCyI_B",
        "outputId": "a02003a3-1c04-4f91-d861-44796b8f1279",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hist Plot for fare_amount\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hist Plot for trip_distance\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hist Plot for extra\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}