{ "cells": [ { "cell_type": "markdown", "id": "ccc704a0", "metadata": { "id": "ccc704a0" }, "source": [ "
\n", "

Python Project Sprint - Part 2

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\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5d30b8a1", "metadata": { "id": "5d30b8a1" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "pd.set_option('display.max_columns', None)" ] }, { "cell_type": "markdown", "id": "baec20d3", "metadata": { "id": "baec20d3" }, "source": [ "# E-commerce Dataset Overview\n", "\n", "In this Python Project Sprint, we'll be working with a dataset derived from an online e-commerce platform. The dataset captures customer purchase records, reflecting the shopping behavior and interactions of users. It includes important details such as purchase dates, product categories, amounts spent, customer demographics, and payment methods.\n", "\n", "This dataset is a valuable tool for:\n", "\n", "1. Identifying popular product categories and purchase trends.\n", "2. Analyzing customer spending patterns and behaviors.\n", "3. Examining transaction frequency and payment preferences.\n", "4. Building recommender systems and planning marketing strategies.\n", "\n", "You can find the data dictionary for this dataset below.\n", "\n", "---\n", "\n", "## Data Dictionary\n", "\n", "| Column Name | Data Type | Description |\n", "|---------------------------|-----------|-------------------------------------------------------------------------------------------------|\n", "| `Customer ID` | int | Unique identifier for each customer. |\n", "| `Age` | string | Customer's age in years. |\n", "| `Gender` | string | Gender of the customer. |\n", "| `Item Purchased` | string | Name of the item purchased by the customer. |\n", "| `Category` | string | Product category the item belongs to. |\n", "| `Purchase Amount (USD)` | float | The amount of money spent on the purchase, in USD. |\n", "| `Location` | string | The customer's location (state or region). |\n", "| `Size` | string | Size of the purchased item (e.g., S, M, L). |\n", "| `Color` | string | Color of the purchased item. |\n", "| `Discount Applied` | string | Indicates if a discount was applied (\"Yes\" or \"No\"). |\n", "| `Promo Code Used` | string | Indicates if a promotional code was used (\"Yes\" or \"No\"). |\n", "| `Previous Purchases` | float | Number of previous purchases made by the customer. |\n", "| `Payment Method` | string | Payment method used by the customer. |\n", "| `Frequency of Purchases` | string | Frequency of purchases by the customer (e.g., Weekly, Monthly). |\n", "| `user id` | float | ID of the user associated with the purchase. |\n", "| `product id` | string | Unique identifier for the purchased product. |\n", "| `Interaction type` | string | Type of interaction the customer had with the product (e.g., purchase, view, like). |\n", "| `Time stamp` | string | Date and time of the transaction or interaction (in DD/MM/YYYY HH:MM format). |" ] }, { "cell_type": "markdown", "id": "831a627a", "metadata": { "id": "831a627a" }, "source": [ "# Data Visualizations" ] }, { "cell_type": "markdown", "id": "e7dfda8a", "metadata": { "id": "e7dfda8a" }, "source": [ "In part 1 of our Python Project Sprint, we went through the cleaning process of the e-commerce dataset and saved it as a new `csv` file. This is good practice, as data analysis pipelines are typically built in a modular way with cleaning performed first. The cleaned dataset is then stored and used in a separate module for business analysis and visualization.\n", "\n", "We will begin by creating a Python function that can take in any raw dataset (such as our uncleaned e-commerce dataset), perform all cleaning steps, and store it for analysis. Then, we will go through the process of visualization to create charts that can help us delve into the data and extract insights from it." ] }, { "cell_type": "markdown", "id": "9479a5db", "metadata": { "id": "9479a5db" }, "source": [ "## Data Cleaning Pipeline" ] }, { "cell_type": "markdown", "id": "a5a2bac8", "metadata": { "id": "a5a2bac8" }, "source": [ "Let's create a function to automate our data cleaning process. This function will apply all the steps we've discussed to a raw dataset and return the cleaned data. It takes five parameters:\n", "\n", "1. `dataframe`: The input DataFrame to be cleaned.\n", "2. `drop_duplicates`: Boolean indicating whether to remove duplicate rows.\n", "3. `remove_missing`: Boolean indicating whether to remove rows with missing values.\n", "4. `dropping_columns`: List of column names to be dropped.\n", "5. `renaming_columns`: Dictionary of columns that should be renamed.\n", "\n", "This function will streamline our data preparation workflow, making it easier to consistently clean multiple datasets.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "ef5857cd", "metadata": { "id": "ef5857cd" }, "outputs": [], "source": [ "# Cleaning function\n", "\n", "def clean_dataframe(df:pd.DataFrame(), drop_duplicates:bool =True, remove_missing:bool =True, dropping_columns:list = None,\n", " renaming_columns:dict={}) -> pd.DataFrame():\n", " \"\"\"\n", " Cleans the given dataframe by performing the following operations:\n", "\n", " - Drops specified columns.\n", " - Removes missing values if specified.\n", " - Removes duplicate rows if specified.\n", " - Replaces ',' with '.' in the 'Age' column.\n", " - Replaces '\\n' with '' in the 'Payment Method' column.\n", " - Drops columns if specified.\n", " - Renames columns if specified.\n", " - Converts all object columns to lowercase.\n", "\n", " Parameters:\n", " df (pd.DataFrame): The dataframe to be cleaned.\n", " drop_duplicates (bool): If True, remove duplicate rows. Default is True.\n", " remove_missing (bool): If True, remove rows with missing values. Default is True.\n", " dropping_columns (list): Name of the columns that have to get dropped.\n", " renaming_columns (dict): Dictionary with the old and new name of the columns.\n", "\n", " Returns:\n", " pd.DataFrame: The cleaned dataframe.\n", "\n", " \"\"\"\n", "\n", " # Step 1: drop column\n", " df.drop(columns = dropping_columns, axis = 1, inplace = True)\n", "\n", "\n", " # Step 2: Remove missing values\n", " if remove_missing:\n", " df = df.dropna()\n", "\n", " # Step 3: Remove duplicates\n", " if drop_duplicates:\n", " df = df.drop_duplicates()\n", "\n", " # Step 4: Replace commas with periods in 'Age' column (ensure the column exists)\n", " if 'Age' in df.columns:\n", " df['Age'] = df['Age'].str.replace(',', '.')\n", "\n", " # Step 5: Remove newlines in 'Payment Method' column (ensure the column exists)\n", " if 'Vendor' in df.columns:\n", " df['Payment Method'] = df['Payment Method'].str.replace('\\n', '')\n", "\n", " # Step 6: Convert all object columns to lowercase\n", " for col in df.select_dtypes(include=['object']).columns:\n", " df[col] = df[col].str.lower()\n", "\n", " # Step 7: Rename all columns that are specified in renaming columns parameter\n", " df.rename(columns=renaming_columns, inplace=True)\n", "\n", " # Step8: Reindexing and sorting\n", " df.set_index('Customer ID', inplace=True, drop=True)\n", " df.sort_index(inplace=True)\n", "\n", " return df\n" ] }, { "cell_type": "code", "execution_count": null, "id": "3c95ddf7", "metadata": { "id": "3c95ddf7", "outputId": "757e4c02-5239-4a98-dd27-be54061fe457" }, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0Customer IDAgeGenderItem PurchasedCategoryPurchase Amount (USD)LocationSizeColorSeasonReview RatingSubscription StatusShipping TypeDiscount AppliedPromo Code UsedPrevious PurchasesPayment MethodFrequency of Purchasesuser idproduct idInteraction typeTime stampUnnamed: 4
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" ], "text/plain": [ " Unnamed: 0 Customer ID Age Gender Item Purchased Category \\\n", "0 0 1 55,0 Male bLOuSE Clothing \n", "1 1 2 19,0 Male SWEaTer Clothing \n", "2 2 3 50,0 Male jeaNS Clothing \n", "3 3 4 21,0 Male saNdals Footwear \n", "4 4 5 45,0 Male BLoUSE Clothing \n", "\n", " Purchase Amount (USD) Location Size Color Season \\\n", "0 53 KeNtUCkY L Gray Winter \n", "1 64 MaINe L Maroon Winter \n", "2 73 MASSAcHUsetTs S Maroon Spring \n", "3 90 RHodE IsLAnD M Maroon Spring \n", "4 49 OReGON M Turquoise Spring \n", "\n", " Review Rating Subscription Status Shipping Type Discount Applied \\\n", "0 3.1 Yes Express Yes \n", "1 3.1 Yes Express Yes \n", "2 3.1 Yes Free Shipping Yes \n", "3 3.5 Yes Next Day Air Yes \n", "4 2.7 Yes Free Shipping Yes \n", "\n", " Promo Code Used Previous Purchases Payment Method Frequency of Purchases \\\n", "0 Yes 14 Venmo\\n Fortnightly \n", "1 Yes 2 Cash\\n Fortnightly \n", "2 Yes 23 Credit Card\\n Weekly \n", "3 Yes 49 PayPal\\n Weekly \n", "4 Yes 31 PayPal\\n Annually \n", "\n", " user id product id Interaction type \\\n", "0 1.0 4c69b61db1fc16e7013b43fc926e502d purchase \n", "1 2.0 66d49bbed043f5be260fa9f7fbff5957 view \n", "2 3.0 2c55cae269aebf53838484b0d7dd931a like \n", "3 4.0 18018b6bc416dab347b1b7db79994afa view \n", "4 5.0 e04b990e95bf73bbe6a3fa09785d7cd0 like \n", "\n", " Time stamp Unnamed: 4 \n", "0 10/10/2023 8:00 NaN \n", "1 11/10/2023 8:00 NaN \n", "2 12/10/2023 8:00 NaN \n", "3 13/10/2023 8:00 NaN \n", "4 14/10/2023 8:00 NaN " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the raw dataset once again\n", "df = pd.read_csv('data/e-commerce.csv')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "f48a43f7", "metadata": { "id": "f48a43f7", "outputId": "e7c68be0-ba00-43c5-8d60-c4999380a013" }, "outputs": [ { "data": { "text/html": [ "
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AgeGenderItem PurchasedCategoryPurchase Amount (USD)LocationSizeColorSeasonReview RatingSubscription StatusShipping TypeDiscount AppliedPromo Code UsedPrevious PurchasesPayment MethodFrequency of PurchasesInteraction typetimestamp
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155.0maleblouseclothing53kentuckylgraywinter3.1yesexpressyesyes14venmo\\nfortnightlypurchase10/10/2023 8:00
219.0malesweaterclothing64mainelmaroonwinter3.1yesexpressyesyes2cash\\nfortnightlyview11/10/2023 8:00
350.0malejeansclothing73massachusettssmaroonspring3.1yesfree shippingyesyes23credit card\\nweeklylike12/10/2023 8:00
421.0malesandalsfootwear90rhode islandmmaroonspring3.5yesnext day airyesyes49paypal\\nweeklyview13/10/2023 8:00
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" ], "text/plain": [ " Age Gender Item Purchased Category Purchase Amount (USD) \\\n", "Customer ID \n", "1 55.0 male blouse clothing 53 \n", "2 19.0 male sweater clothing 64 \n", "3 50.0 male jeans clothing 73 \n", "4 21.0 male sandals footwear 90 \n", "5 45.0 male blouse clothing 49 \n", "\n", " Location Size Color Season Review Rating \\\n", "Customer ID \n", "1 kentucky l gray winter 3.1 \n", "2 maine l maroon winter 3.1 \n", "3 massachusetts s maroon spring 3.1 \n", "4 rhode island m maroon spring 3.5 \n", "5 oregon m turquoise spring 2.7 \n", "\n", " Subscription Status Shipping Type Discount Applied \\\n", "Customer ID \n", "1 yes express yes \n", "2 yes express yes \n", "3 yes free shipping yes \n", "4 yes next day air yes \n", "5 yes free shipping yes \n", "\n", " Promo Code Used Previous Purchases Payment Method \\\n", "Customer ID \n", "1 yes 14 venmo\\n \n", "2 yes 2 cash\\n \n", "3 yes 23 credit card\\n \n", "4 yes 49 paypal\\n \n", "5 yes 31 paypal\\n \n", "\n", " Frequency of Purchases Interaction type timestamp \n", "Customer ID \n", "1 fortnightly purchase 10/10/2023 8:00 \n", "2 fortnightly view 11/10/2023 8:00 \n", "3 weekly like 12/10/2023 8:00 \n", "4 weekly view 13/10/2023 8:00 \n", "5 annually like 14/10/2023 8:00 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's test the function\n", "df_cleaned = clean_dataframe(df, drop_duplicates=True, remove_missing=True,\n", " dropping_columns=['Unnamed: 0','product id', 'user id', 'Unnamed: 4'],\n", " renaming_columns={'Time stamp':'timestamp'})\n", "df_cleaned.head()" ] }, { "cell_type": "markdown", "id": "41aec850", "metadata": { "id": "41aec850" }, "source": [ "We can adjust the type of the columns after cleaning or even add them to the function." ] }, { "cell_type": "markdown", "id": "78a11e96", "metadata": { "id": "78a11e96" }, "source": [ "## Visualizations and Insights" ] }, { "cell_type": "markdown", "id": "f5e7a154", "metadata": { "id": "f5e7a154" }, "source": [ "With our dataset now cleaned and processed, we're ready to dive into the analysis. We'll use data visualization techniques to uncover trends and patterns. This analysis will provide valuable insights to achieve the project's goals. We'll use `matplotlib` to create our visualizations.\n", "\n", "Let's first load the cleaned version of the dataset." ] }, { "cell_type": "code", "execution_count": null, "id": "43aa7855", "metadata": { "id": "43aa7855", "outputId": "e9d5067d-522f-40fa-a39c-05ae45194ff5" }, "outputs": [ { "data": { "text/html": [ "
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AgeGenderItem PurchasedCategoryPurchase Amount (USD)LocationSizeColorSeasonReview RatingSubscription StatusShipping TypeDiscount AppliedPromo Code UsedPrevious PurchasesPayment MethodFrequency of PurchasesInteraction typetimestampMonth_NameWeekday_Name
Customer ID
155MaleBlouseClothing53KentuckyLGrayWinter3.1YesExpressYesYes14VenmoFortnightlypurchase2023-10-10 08:00:00OctoberTuesday
219MaleSweaterClothing64MaineLMaroonWinter3.1YesExpressYesYes2CashFortnightlyview2023-10-11 08:00:00OctoberWednesday
350MaleJeansClothing73MassachusettsSMaroonSpring3.1YesFree ShippingYesYes23Credit cardWeeklylike2023-10-12 08:00:00OctoberThursday
421MaleSandalsFootwear90Rhode islandMMaroonSpring3.5YesNext Day AirYesYes49PaypalWeeklyview2023-10-13 08:00:00OctoberFriday
545MaleBlouseClothing49OregonMTurquoiseSpring2.7YesFree ShippingYesYes31PaypalAnnuallylike2023-10-14 08:00:00OctoberSaturday
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" ], "text/plain": [ " Age Gender Item Purchased Category Purchase Amount (USD) \\\n", "Customer ID \n", "1 55 Male Blouse Clothing 53 \n", "2 19 Male Sweater Clothing 64 \n", "3 50 Male Jeans Clothing 73 \n", "4 21 Male Sandals Footwear 90 \n", "5 45 Male Blouse Clothing 49 \n", "\n", " Location Size Color Season Review Rating \\\n", "Customer ID \n", "1 Kentucky L Gray Winter 3.1 \n", "2 Maine L Maroon Winter 3.1 \n", "3 Massachusetts S Maroon Spring 3.1 \n", "4 Rhode island M Maroon Spring 3.5 \n", "5 Oregon M Turquoise Spring 2.7 \n", "\n", " Subscription Status Shipping Type Discount Applied \\\n", "Customer ID \n", "1 Yes Express Yes \n", "2 Yes Express Yes \n", "3 Yes Free Shipping Yes \n", "4 Yes Next Day Air Yes \n", "5 Yes Free Shipping Yes \n", "\n", " Promo Code Used Previous Purchases Payment Method \\\n", "Customer ID \n", "1 Yes 14 Venmo \n", "2 Yes 2 Cash \n", "3 Yes 23 Credit card \n", "4 Yes 49 Paypal \n", "5 Yes 31 Paypal \n", "\n", " Frequency of Purchases Interaction type timestamp \\\n", "Customer ID \n", "1 Fortnightly purchase 2023-10-10 08:00:00 \n", "2 Fortnightly view 2023-10-11 08:00:00 \n", "3 Weekly like 2023-10-12 08:00:00 \n", "4 Weekly view 2023-10-13 08:00:00 \n", "5 Annually like 2023-10-14 08:00:00 \n", "\n", " Month_Name Weekday_Name \n", "Customer ID \n", "1 October Tuesday \n", "2 October Wednesday \n", "3 October Thursday \n", "4 October Friday \n", "5 October Saturday " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the dataset\n", "df = pd.read_csv('data/cleaned-e-commerce.csv', index_col=0)\n", "\n", "# Evaluate the dataset\n", "df.head()" ] }, { "cell_type": "markdown", "id": "99f2d4f1", "metadata": { "id": "99f2d4f1" }, "source": [ "Let's first examine the distribution of ratings across various items and categories. This will help us analyze customer satisfaction levels." ] }, { "cell_type": "code", "execution_count": null, "id": "d09e0064", "metadata": { "id": "d09e0064", "outputId": "3be7ba49-a8e9-43be-c579-4b3286119902" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot 1: Distribution of Review Ratings\n", "plt.figure(figsize=(10, 6))\n", "plt.hist(df['Review Rating'], bins=20, edgecolor='black')\n", "plt.title('Distribution of Review Ratings')\n", "plt.xlabel('Review Rating')\n", "plt.ylabel('Frequency')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a8b60e4a", "metadata": { "id": "a8b60e4a" }, "source": [ "The histogram shows a relatively uniform distribution, indicating that there is no general pattern in terms of the reviews received. There are almost an equal number of products that received a rating above 3.5 and those that received a rating below 3.5. This shows that we need to understand the root cause of lower ratings and try to improve them to have a distribution skewed towards higher ratings.\n", "\n", "Next, let's explore the frequency of purchases to see how often customers shop on the platform." ] }, { "cell_type": "code", "execution_count": null, "id": "1352cdde", "metadata": { "id": "1352cdde", "outputId": "1ac8e010-0393-4685-ecd2-6e279977b961" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot 2: Frequency of purchases by customers\n", "plt.figure(figsize=(10, 6))\n", "df['Frequency of Purchases'].value_counts().plot(kind='bar', edgecolor='black')\n", "plt.title('Frequency of Purchases by Customers')\n", "plt.xlabel('Purchase Frequency')\n", "plt.ylabel('Number of Customers')\n", "plt.xticks(rotation=45)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f09f91c9", "metadata": { "id": "f09f91c9" }, "source": [ "This bar chart shows that the most common purchase frequencies are **every 3 months** and **quarterly**. Fewer customers shop weekly or bi-weekly, indicating that the platform does not have a high number of regular buyers, which could imply a lack of customer loyalty.\n", "\n", "In order to improve our customer satisfaction, we need to understand the times of year when we have the highest sales and also analyze the geographic distribution of our customers. Let's find the distribution of sales over the months as well as the top 10 geographic locations with the highest number of customers." ] }, { "cell_type": "code", "execution_count": null, "id": "ecaadc0b", "metadata": { "id": "ecaadc0b", "outputId": "6593cf41-afe2-4296-c20c-8c3249994570" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot 3: Distribution of sales over month\n", "monthly_purchases = df.groupby('Month_Name').size().sort_values(ascending=False)\n", "\n", "# Plot the bar chart\n", "plt.figure(figsize=(10, 6))\n", "monthly_purchases.plot(kind='bar', edgecolor='black')\n", "plt.title('Total Items Purchased Per Month')\n", "plt.xlabel('Month')\n", "plt.ylabel('Number of Purchases')\n", "plt.xticks(rotation=45)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f5829271", "metadata": { "id": "f5829271" }, "source": [ "The results show that December, November, and January have the highest number of sales, likely due to the Christmas holidays. This presents an opportunity for the marketing team to increase spending during this time to attract even more customers. Additionally, we can see that September and August have the lowest number of sales." ] }, { "cell_type": "markdown", "id": "ff0a4aac", "metadata": { "id": "ff0a4aac" }, "source": [ "Let's examine the top 10 States in terms of number of sales. " ] }, { "cell_type": "code", "execution_count": null, "id": "2e65c0c9", "metadata": { "id": "2e65c0c9", "outputId": "586ebfa0-c854-44f7-c577-15cf214513b2" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot 4: Top 10 Geographic location of customers\n", "plt.figure(figsize=(10, 6))\n", "\n", "top_10 = df['Location'].value_counts().sort_values(ascending=False).head(10)\n", "\n", "plt.bar(top_10.index, top_10.values)\n", "plt.title('Geographic Distribution of Customers')\n", "plt.xlabel('Location')\n", "plt.ylabel('Number of Customers')\n", "plt.xticks(rotation=90)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "eee31645", "metadata": { "id": "eee31645" }, "source": [ "In terms of geographic locations, we can see that there is a relatively uniform distribution, meaning the sales among the states are not significantly different. That being said, California, Delaware, Montana, and Maryland are among the top four states with the highest sales, which the marketing team can focus on to improve customer satisfaction.\n", "\n", "Let's now analyze the data by product category, which can provide a more detailed understanding of spending habits." ] }, { "cell_type": "code", "execution_count": null, "id": "2c22e080", "metadata": { "id": "2c22e080", "outputId": "d89e6729-bf1f-4222-979b-25f46eb8ef09" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot 5: Most purchased product categories\n", "plt.figure(figsize=(10, 6))\n", "product_categories = df['Category'].value_counts().sort_values(ascending=False)\n", "\n", "plt.bar(product_categories.index, product_categories.values)\n", "plt.title('Most Purchased Product Categories')\n", "plt.xlabel('Product Category')\n", "plt.ylabel('Number of Purchases')\n", "plt.xticks(rotation=45)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "61ffd1be", "metadata": { "id": "61ffd1be" }, "source": [ "This bar chart shows that the most frequently purchased category is Clothing, followed by Accessories. This suggests that clothing items dominate the e-commerce platform's sales, while categories like Footwear and Outerwear see comparatively fewer purchases. Since these are the top-selling categories, we need to look into the rating distribution for them and see if there are any differences between them in terms of customer review ratings.\n", "\n", "In our next and final visualization, we will look into the rating distribution across various categories. To do this, we use a specific type of chart called **Box Plot**. `plt.boxplot` creates this chart for us. To use it, we need to extract the ratings for each category and put them into a `list`." ] }, { "cell_type": "code", "execution_count": null, "id": "82dab57f", "metadata": { "id": "82dab57f", "outputId": "4c5b6bab-45b9-4b74-b76b-dd1a9301dc71" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot 6: Ratings distribution across product categories\n", "categories = df['Category'].unique()\n", "category_groups = [df[df['Category'] == category]['Review Rating'] for category in categories]\n", "\n", "\n", "plt.figure(figsize=(10, 6))\n", "plt.boxplot(category_groups, labels=categories)\n", "plt.title('Review Rating by Product Category')\n", "plt.xlabel('Product Category')\n", "plt.ylabel('Review Rating')\n", "plt.xticks(rotation=45)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1db940ab", "metadata": { "id": "1db940ab" }, "source": [ "Each box represents the distribution of review ratings for a specific product category. The top and bottom lines indicate the minimum and maximum ratings that each category received. The middle line inside each box represents the median review rating for that category. As we can see, there is no significant difference between the medians of the categories, suggesting a similar rating distribution across all categories. Notably, for all categories, ratings are centered around 2.5.\n", "\n", "It appears that there is no difference in customer ratings across various categories." ] }, { "cell_type": "markdown", "id": "8f15ccb8", "metadata": { "id": "8f15ccb8" }, "source": [ "# Conclusions and Next Steps" ] }, { "cell_type": "markdown", "id": "8d1f23df", "metadata": { "id": "8d1f23df" }, "source": [ "In this project, we analyzed an e-commerce dataset to uncover key insights into customer purchasing behaviors. We explored product categories, review ratings, purchase frequency, and geographic distribution, gaining insights into customer preferences and engagement trends. These insights help identify which product categories are most popular, how frequently customers make purchases, and how satisfied they are with their purchases.\n", "\n", "We found that **Clothing** and **Accessories** are the top two purchased categories, and that the review ratings across all categories are almost identical. To improve the customer experience, we now need to dive into the reviews left for each product to understand the reasons behind the low ratings. This analysis has helped us narrow down our focus to the most popular categories and locations.\n", "\n", "Data analysts and scientists will take this analysis to the next step by creating a dashboard that displays all these metrics and charts in an interactive and dynamic report. They will perform in-depth statistical analysis to understand the factors impacting the low ratings and then create a machine learning model that can predict the rating for an item before it is released." ] }, { "cell_type": "markdown", "id": "b6fada3b", "metadata": { "id": "b6fada3b" }, "source": [ "
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" ] } ], "metadata": { "colab": { "provenance": [] }, "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": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 5 }