diff --git a/.virtual_documents/notebooks/Airline_Project1.ipynb b/.virtual_documents/notebooks/Airline_Project1.ipynb
new file mode 100644
index 00000000..f11919e4
--- /dev/null
+++ b/.virtual_documents/notebooks/Airline_Project1.ipynb
@@ -0,0 +1,45 @@
+import pandas as pd
+import numpy as np
+
+df=pd.read_csv('train.csv')
+df
+
+
+binary_change= {
+ 'satisfied': 1,
+ 'neutral or dissatisfied': 0,
+}
+
+df['satisfaction'] = df['satisfaction'].replace(binary_change)
+df.drop(columns=['Unnamed: 0'], inplace=True)
+
+
+df
+
+
+df['satisfaction'].value_counts()
+
+
+df['Customer Type'].value_counts()
+
+
+dfSatisfied=df[df['satisfaction'] == 1]
+dfDisatisfied=df[df['satisfaction'] == 0]
+
+
+
+
+
+dfSatisfied.describe()
+
+
+dfDisatisfied.describe()
+
+
+dfSatisfied
+
+
+dfDisatisfied
+
+
+
diff --git a/.virtual_documents/notebooks/Untitled.ipynb b/.virtual_documents/notebooks/Untitled.ipynb
new file mode 100644
index 00000000..d465ca0d
--- /dev/null
+++ b/.virtual_documents/notebooks/Untitled.ipynb
@@ -0,0 +1,41 @@
+import pandas as pd
+import numpy as np
+
+df=pd.read_csv('train.csv')
+df
+
+
+binary_change= {
+ 'satisfied': 1,
+ 'neutral or dissatisfied': 0,
+}
+
+df['satisfaction'] = df['satisfaction'].replace(binary_change)
+
+
+
+
+
+df['satisfaction'].value_counts()
+
+
+df['Customer Type'].value_counts()
+
+
+dfSatisfied=df[df['satisfaction'] == 'satisfied']
+dfDisatisfied=df[df['satisfaction'] == 'neutral or dissatisfied']
+
+
+dfSatisfied.describe()
+
+
+dfDisatisfied.describe()
+
+
+dfSatisfied
+
+
+dfDisatisfied
+
+
+
diff --git a/Airline_Project1_charles.ipynb b/Airline_Project1_charles.ipynb
new file mode 100644
index 00000000..7d17001e
--- /dev/null
+++ b/Airline_Project1_charles.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d4ad8958-e5ec-4677-a12e-83688dec0f9f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from scipy import stats\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "df=pd.read_csv('train.csv')\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "792266fb-4e25-46b0-958a-4825e5241075",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Assuming your DataFrame is named 'df'\n",
+ "print(\"--- DataFrame Information (Column Names, Non-Null Count, and Dtype) ---\")\n",
+ "df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ea9e5d1-19c8-486d-bae0-d136460cdad2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"\\n--- List of Column Names (Original) ---\")\n",
+ "print(df.columns.tolist())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c2d45bee-22c8-46f2-81cd-fa8c5354b6ac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"\\n--- Data Types (Dtypes) per Column ---\")\n",
+ "print(df.dtypes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3deb81bb-1755-496e-ace4-d012888c2dea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.columns = df.columns.str.strip()\n",
+ "\n",
+ "# Convert Target Variable to Numerical \n",
+ "df['is_satisfied'] = df['satisfaction'].map({\n",
+ " 'satisfied': 1,\n",
+ " 'neutral or dissatisfied': 0\n",
+ "})\n",
+ "\n",
+ "# Identify numerical columns for cleaning and scaling\n",
+ "numerical_cols = ['Age', 'Flight Distance', 'Departure Delay in Minutes', 'Arrival Delay in Minutes']\n",
+ "\n",
+ "# CRITICAL FIX: Handle NaN values BEFORE scaling, using the recommended pattern.\n",
+ "# We will use the median for imputation.\n",
+ "print(\"--- Fixing Missing Values ---\")\n",
+ "for col in numerical_cols:\n",
+ " if df[col].isnull().sum() > 0:\n",
+ " median_value = df[col].median()\n",
+ " # FIX: Assign the result of fillna() back to the column, removing inplace=True\n",
+ " df[col] = df[col].fillna(median_value)\n",
+ " print(f\"Imputed {col} with the median ({median_value}).\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d9c9dbc0-251e-4c6b-baed-7b38212c9404",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"\\n--- 3. Numerical Columns After Scaling (MinMaxScaler) ---\")\n",
+ "\n",
+ "# Initialize the MinMaxScaler\n",
+ "scaler = MinMaxScaler()\n",
+ "\n",
+ "# Fit and transform the data\n",
+ "# Since NaNs were fixed above, this should now run without issues.\n",
+ "df[numerical_cols] = scaler.fit_transform(df[numerical_cols])\n",
+ "\n",
+ "print(df[numerical_cols].head())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c7a852a3-790e-422c-a969-8e175a4e74ca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A. Correlation with Satisfaction (Pandas)\n",
+ "print(\"\\n--- 4. Correlation with satisfaction ---\")\n",
+ "\n",
+ "# Select the numerical and ordinal columns, plus the target variable\n",
+ "correlation_cols = [\n",
+ " 'is_satisfied', 'Age', 'Flight Distance', \n",
+ " 'Inflight wifi service', 'Cleanliness', 'Departure Delay in Minutes'\n",
+ "] \n",
+ "\n",
+ "# Calculate the correlation matrix for selected columns\n",
+ "correlation_matrix = df[correlation_cols].corr()\n",
+ "\n",
+ "# Print the correlation of each factor with 'is_satisfied'\n",
+ "print(correlation_matrix['is_satisfied'].sort_values(ascending=False))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9d267c6d-213e-4096-be1f-136e4ce9427a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# B. T-test on Delays (SciPy Stats)\n",
+ "satisfied_delay = df[df['is_satisfied'] == 1]['Arrival Delay in Minutes']\n",
+ "dissatisfied_delay = df[df['is_satisfied'] == 0]['Arrival Delay in Minutes']\n",
+ "\n",
+ "# Perform independent two-sample T-test\n",
+ "t_stat, p_value = stats.ttest_ind(satisfied_delay, dissatisfied_delay, equal_var=False)\n",
+ "\n",
+ "print(\"\\n--- T-test (Satisfied vs. Dissatisfied Arrival Delay) ---\")\n",
+ "print(f\"T-Statistic: {t_stat:.2f}\")\n",
+ "print(f\"P-Value: {p_value:.5f}\")\n",
+ "print(\"Conclusion: P-Value < 0.05 suggests a statistically significant difference in mean delay.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c7b46a7-53e0-4741-8d82-28ee5e63437d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Configure plot style\n",
+ "sns.set_style(\"whitegrid\")\n",
+ "plt.figure(figsize=(18, 6))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b6c8d766-b7a4-4dbc-b62d-aea79b2cd4dd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a bar plot\n",
+ "# FIX: Explicitly set hue=satisfaction_by_loyalty.index and legend=False\n",
+ "sns.barplot(\n",
+ " x=satisfaction_by_loyalty.index, \n",
+ " y=satisfaction_by_loyalty.values * 100, \n",
+ " hue=satisfaction_by_loyalty.index, # FIX applied here\n",
+ " palette=\"viridis\", \n",
+ " legend=False # FIX applied here\n",
+ ")\n",
+ "\n",
+ "# Set the title and labels\n",
+ "plt.title('Satisfaction Rate by Customer Type', fontsize=14)\n",
+ "plt.xlabel('Customer Type', fontsize=12)\n",
+ "plt.ylabel('Mean Satisfaction Rate (%)', fontsize=12)\n",
+ "\n",
+ "plt.xticks(rotation=15, ha='right') \n",
+ "plt.ylim(0, satisfaction_by_loyalty.values.max() * 100 * 1.1)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0ef5ee37-f20a-4100-8419-4eb3aef26bb9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# B. Heatmap: Feature Correlation \n",
+ "plt.subplot(1, 3, 2)\n",
+ "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n",
+ "plt.title('Feature Correlation Heatmap')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d7ec3f46-16d6-4192-b925-c6d3687ef077",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# C. KDE Plot: Delay Distribution by Satisfaction \n",
+ "plt.subplot(1, 3, 3)\n",
+ "sns.kdeplot(data=df, x='Departure Delay in Minutes', hue='satisfaction', fill=True, common_norm=False)\n",
+ "plt.title('Departure Delay Distribution by satisfaction (Scaled)')\n",
+ "plt.xlim(-0.1, 0.5) \n",
+ "plt.legend(title='satisfaction', labels=df['satisfaction'].unique().tolist())\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e2b97ab-000c-4155-806b-288f230a7937",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "max_scaled_delay = df['Departure Delay in Minutes'].max()\n",
+ "\n",
+ "# Define new bins using scaled values\n",
+ "# Using small fractions (0.0001, 0.01) that represent small delays in the scaled [0, 1] range\n",
+ "delay_bins_fixed = [-1e-5, 0.0001, 0.01, max_scaled_delay] \n",
+ "delay_labels_fixed = ['No Delay', 'Minor Delay (Scaled)', 'Major Delay (Scaled)']\n",
+ "\n",
+ "# Apply the fixed bins\n",
+ "df['Departure Delay Category'] = pd.cut(\n",
+ " df['Departure Delay in Minutes'], \n",
+ " bins=delay_bins_fixed, \n",
+ " labels=delay_labels_fixed,\n",
+ " right=True,\n",
+ " include_lowest=True\n",
+ ")\n",
+ "\n",
+ "# Group by the new category and calculate mean satisfaction (%)\n",
+ "# Note: observed=True is used to suppress the FutureWarning\n",
+ "satisfaction_by_delay_category = df.groupby('Departure Delay Category', observed=True)['is_satisfied'].mean() * 100\n",
+ "\n",
+ "print(\"Calculated Satisfaction Rates by Delay Category:\")\n",
+ "print(satisfaction_by_delay_category)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dcbb8c59-19df-48e0-9c0c-30300226d876",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8, 5))\n",
+ "sns.set_style(\"whitegrid\")\n",
+ "\n",
+ "# Create a bar plot using the index (categories) and values (percentages)\n",
+ "sns.barplot(\n",
+ " x=satisfaction_by_delay_category.index, \n",
+ " y=satisfaction_by_delay_category.values, \n",
+ " # Use the index for hue to color bars based on the category (fixes FutureWarning)\n",
+ " hue=satisfaction_by_delay_category.index,\n",
+ " palette=\"magma\",\n",
+ " legend=False\n",
+ ")\n",
+ "\n",
+ "# Set the title and labels\n",
+ "plt.title('Satisfaction Rate by Departure Delay Severity', fontsize=14)\n",
+ "plt.xlabel('Departure Delay Category', fontsize=12)\n",
+ "plt.ylabel('Mean Satisfaction Rate (%)', fontsize=12)\n",
+ "\n",
+ "# Rotate x-axis labels for better readability\n",
+ "plt.xticks(rotation=15, ha='right') \n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b74c8b88-9e1b-45e1-879b-03808f867427",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the obstacles and their estimated difficulty scores (1-5)\n",
+ "obstacles_data = {\n",
+ " 'Obstacle': [\n",
+ " 'Handling Ordinal vs. Categorical Variables',\n",
+ " 'Missing Data Imputation Strategy',\n",
+ " 'Outliers in Delay Columns',\n",
+ " 'Inconsistent Column Names and Spacing',\n",
+ " 'Data Type Coercion and Errors'\n",
+ " ],\n",
+ " 'Difficulty_Score': [5, 4, 4, 3, 3] # Scores are subjective for visualization\n",
+ "}\n",
+ "\n",
+ "# Create the DataFrame\n",
+ "df_obstacles = pd.DataFrame(obstacles_data)\n",
+ "\n",
+ "print(\"--- List of Data Cleaning Obstacles ---\")\n",
+ "# Print only the list of obstacle names\n",
+ "print(df_obstacles['Obstacle'].tolist())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b1d5c25-a3c4-467f-8aee-864df29aa25d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a DataFrame for the obstacles and their subjective difficulty scores (1-5)\n",
+ "data = {\n",
+ " 'Obstacle': [\n",
+ " 'Inconsistent Column Names and Spacing',\n",
+ " 'Missing Data Imputation Strategy',\n",
+ " 'Data Type Coercion and Errors',\n",
+ " 'Handling Ordinal vs. Categorical Variables',\n",
+ " 'Outliers in Delay Columns'\n",
+ " ],\n",
+ " 'Difficulty Score (1-5)': [3, 4, 3, 5, 4]\n",
+ "}\n",
+ "df_obstacles = pd.DataFrame(data)\n",
+ "\n",
+ "# Sort the data by Difficulty Score for a clearer bar chart\n",
+ "df_obstacles = df_obstacles.sort_values(by='Difficulty Score (1-5)', ascending=False)\n",
+ "\n",
+ "# --- Visualization ---\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "sns.set_style(\"whitegrid\")\n",
+ "\n",
+ "# Create the bar plot\n",
+ "ax = sns.barplot(\n",
+ " x='Difficulty Score (1-5)',\n",
+ " y='Obstacle',\n",
+ " data=df_obstacles,\n",
+ " palette='Reds_d',\n",
+ " hue='Obstacle', # Use hue to color each bar uniquely (resolves FutureWarning)\n",
+ " legend=False\n",
+ ")\n",
+ "\n",
+ "# Set the title and labels\n",
+ "plt.title('Perceived Difficulty of Data Cleaning Obstacles', fontsize=16)\n",
+ "plt.xlabel('Difficulty Score (1 = Low, 5 = High)', fontsize=12)\n",
+ "plt.ylabel('') # Obstacle names are on the y-axis\n",
+ "\n",
+ "# Ensure labels are visible\n",
+ "plt.xticks(range(1, 6))\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.savefig('cleaning_obstacles_barchart.png')\n",
+ "print(\"Bar chart saved as cleaning_obstacles_barchart.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e6c64f1c-2127-46a0-ac97-9e0d5b2d7bc3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:base] *",
+ "language": "python",
+ "name": "conda-base-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index f637438f..8e15edf6 100644
--- a/README.md
+++ b/README.md
@@ -1,77 +1,101 @@
# Project overview
-...
-# Installation
+**Airline Passenger Satisfaction**
-1. **Clone the repository**:
+1. Problem Statement:
+
+The core problem this project addresses is: Identifying the key drivers of passenger satisfaction and dissatisfaction within an airline's service ecosystem.
-```bash
-git clone https://github.com/YourUsername/repository_name.git
-```
+Airlines need to understand which operational, service, and demographic factors most significantly influence a passenger's overall rating of their experience (Satisfied vs. Neutral or dissatisfied).
+This understanding is crucial for strategic resource allocation, service improvement, and customer retention.
-2. **Install UV**
+2. Dataset Source and Content
-If you're a MacOS/Linux user type:
+Feature: Description
+Source: Typically sourced from passenger surveys collected post-flight. (The uploaded file references a Kaggle dataset).
+Unit of Analysis: An individual flight or passenger survey response.
+Data Types Categorization: Gender, Customer Type, Type of Travel, Class.
+Ordinal: Over 10-15 ratings (e.g., In-flight Wi-Fi, Seat comfort, Food and drink) on a 1-5 scale.
+Numerical: Age, Flight Distance, Departure/Arrival Delay in Minutes.
+Target Variable: Satisfaction (Categorical: Satisfied / Neutral or dissatisfied).
-```bash
-curl -LsSf https://astral.sh/uv/install.sh | sh
-```
-If you're a Windows user open an Anaconda Powershell Prompt and type :
+3. Project Goals (Analytical Objectives)
+The project aims to achieve the following:
-```bash
-powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-```
+Prediction: Build a classification model (e.g., Logistic Regression, Decision Tree, etc.) capable of accurately predicting whether a new passenger will be "Satisfied" or "Neutral/Dissatisfied" based on their flight
+ characteristics and service ratings.
-3. **Create an environment**
+Feature Importance: Determine the top features (e.g., Wi-Fi quality, Cleanliness, Delay duration) that have the strongest correlation and predictive power for overall satisfaction.
-```bash
-uv venv
-```
+Segmentation: Analyze satisfaction patterns across key customer segments, particularly: Loyalty (Loyal Customer vs. disloyal Customer), Travel Purpose (Business travel vs. Personal travel), Travel Class (Business vs. Eco)
-3. **Activate the environment**
-If you're a MacOS/Linux user type (if you're using a bash shell):
+4. Methodology and Techniques Used
+The analysis followed a standard data science pipeline:
-```bash
-source ./venv/bin/activate
-```
+Data Preprocessing: Handled missing values (imputation), cleaned inconsistent data types, and scaled numerical features using MinMaxScaler.
-If you're a MacOS/Linux user type (if you're using a csh/tcsh shell):
+Exploratory Data Analysis (EDA): Used value_counts() and groupby() to understand data distributions and initial segment satisfaction rates.
-```bash
-source ./venv/bin/activate.csh
-```
+Statistical Analysis: Employed Correlation Analysis to measure linear relationships and a T-test to statistically validate the impact of key factors (like delays) on satisfaction.
-If you're a Windows user type:
+Visualization: Utilized Seaborn and Matplotlib to create clear visualizations (Bar Plots, Heatmaps, KDE Plots) to communicate segment differences and feature relationships.
-```bash
-.\venv\Scripts\activate
-```
-4. **Install dependencies**:
+5. Expected Business Impact
+
+The results of this analysis provide the airline with actionable intelligence to drive business decisions:
+
+Prioritization: Allocate investment to service factors with the highest positive correlation to satisfaction (e.g., upgrading in-flight entertainment or Wi-Fi infrastructure).
+
+Operational Focus: Target areas where dissatisfaction is highest (e.g., mitigating minor departure delays) to maximize customer experience per dollar spent.
+
+Marketing & Strategy: Customize service offerings and marketing campaigns based on loyalty and travel purpose (e.g., developing specific retention strategies for disloyal passengers).
-```bash
-uv pip install -r requirements.txt
-```
# Questions
-...
+This dataset is rich with possibilities because it contains both operational factors (delays, distance) and subjective service ratings. The questions were grouped into four main analytical categories: Predictive Modeling, Segmentation, Operational Insight, and Service Quality.
+
+I. Predictive Modeling: Which specific service rating (e.g., In-flight Wi-Fi, Cleanliness) is the single strongest predictor of overall satisfaction?
-# Dataset
-...
+II. Segmentation: What is the percentage difference in satisfaction between Loyal Customers and disloyal Customers, and do these two groups cite different reasons for their satisfaction/dissatisfaction?
+
+III. Operational Insight: What is the precise number of minutes of delay after which the probability of a passenger being "Neutral or dissatisfied" increases most sharply?
+
+IV. Service Quality: If the airline were to improve one service rating by one point (e.g., from 3 to 4), which service provides the greatest return on investment in terms of increased satisfaction?
+
+# Dataset
+Airline Passenger Satisfaction Survey, Kaggle.com train_csv
## Main dataset issues
+The Dataset was largely cleaned which made the analysis easier and straightforward. This notwithstanding, there were some Dataset issues that we had to deal with. These dataset issues included the following:
+
+1. The Satisfaction column contained only strings
+2. The Age column had too many information
+3. The column named Flight Distance also had too many information
+4. Lastly the first dataset selected on day 1 was not reliable and full of synthetic errors
-- ...
-- ...
-- ...
## Solutions for the dataset issues
-...
+Having encountered the above dataset issues a number of solutions were employed to address the challenges. Some of the solutions included the following:
+
+1. We used binary to solve the the Satisfaction column which was string
+
+2. Aggregation and groupings were employed in relation to the Age and Flight Distance columns
+
+3. New column was created to cater for ....
+
+4. The first unreliable dataset was entirely dropped for a new dataset
# Conclussions
-...
+
+The overall conclusion is that Customer Loyalty and Quality In-Flight Service are the primary drivers of satisfaction, while operational failures (specifically delays) are the primary drivers of dissatisfaction.
# Next steps
-...
+
+To maximize overall passenger satisfaction, it was recommended that the airline should adopt a dual strategy:
+
+Service Excellence: Continuously improve and invest in in-flight amenities (Wi-Fi and entertainment) as these yield the highest returns on positive sentiment.
+
+Operational Reliability: Treat on-time performance as a non-negotiable factor. Focus resources on minimizing all delays, as even small disruptions create a disproportionately large increase in dissatisfaction, especially among customers who are not already loyal.
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index 00000000..01aa4ec6
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diff --git a/figures/dissatisfied_customers_flight_distance_percentage_bar_chart.png b/figures/dissatisfied_customers_flight_distance_percentage_bar_chart.png
new file mode 100644
index 00000000..27177a87
Binary files /dev/null and b/figures/dissatisfied_customers_flight_distance_percentage_bar_chart.png differ
diff --git a/figures/dissatisfied_customers_flight_distance_percentage_bar_chart_final.png b/figures/dissatisfied_customers_flight_distance_percentage_bar_chart_final.png
new file mode 100644
index 00000000..898aba05
Binary files /dev/null and b/figures/dissatisfied_customers_flight_distance_percentage_bar_chart_final.png differ
diff --git a/figures/dissatisfied_customers_gender_percentage_bar_chart.png b/figures/dissatisfied_customers_gender_percentage_bar_chart.png
new file mode 100644
index 00000000..216bcb88
Binary files /dev/null and b/figures/dissatisfied_customers_gender_percentage_bar_chart.png differ
diff --git a/figures/figure.png b/figures/figure.png
deleted file mode 100644
index e69de29b..00000000
diff --git a/notebooks/Airline_Project1-Copy_marta.ipynb b/notebooks/Airline_Project1-Copy_marta.ipynb
new file mode 100644
index 00000000..7c315000
--- /dev/null
+++ b/notebooks/Airline_Project1-Copy_marta.ipynb
@@ -0,0 +1,1940 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "561808a4-27ef-4523-892a-8584a31efefa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ "
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+ " \n",
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+ " Baggage handling | \n",
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+ " Arrival Delay in Minutes | \n",
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\n",
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\n",
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+ " 4 | \n",
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\n",
+ " \n",
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+ " ... | \n",
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+ " ... | \n",
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+ " ... | \n",
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+ " ... | \n",
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+ " ... | \n",
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\n",
+ " \n",
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+ " Female | \n",
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+ " 23 | \n",
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+ " 1 | \n",
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+ " 3 | \n",
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+ "
\n",
+ " \n",
+ " | 103900 | \n",
+ " 103900 | \n",
+ " 73097 | \n",
+ " Male | \n",
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+ " satisfied | \n",
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\n",
+ " \n",
+ " | 103901 | \n",
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+ " Male | \n",
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\n",
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+ " Business travel | \n",
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\n",
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+ " Business travel | \n",
+ " Business | \n",
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+ " 3 | \n",
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+ " 1 | \n",
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+ " neutral or dissatisfied | \n",
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103904 rows × 25 columns
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+ "
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+ "\n",
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+ " \n",
+ " \n",
+ " | \n",
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+ " id | \n",
+ " Age | \n",
+ " Flight Distance | \n",
+ " Inflight wifi service | \n",
+ " Departure/Arrival time convenient | \n",
+ " Ease of Online booking | \n",
+ " Gate location | \n",
+ " Food and drink | \n",
+ " Online boarding | \n",
+ " Seat comfort | \n",
+ " Inflight entertainment | \n",
+ " On-board service | \n",
+ " Leg room service | \n",
+ " Baggage handling | \n",
+ " Checkin service | \n",
+ " Inflight service | \n",
+ " Cleanliness | \n",
+ " Departure Delay in Minutes | \n",
+ " Arrival Delay in Minutes | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 45025.000000 | \n",
+ " 44897.000000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 51789.242754 | \n",
+ " 65512.609928 | \n",
+ " 41.750583 | \n",
+ " 1530.140255 | \n",
+ " 3.161288 | \n",
+ " 2.970305 | \n",
+ " 3.031582 | \n",
+ " 2.977879 | \n",
+ " 3.521310 | \n",
+ " 4.027474 | \n",
+ " 3.966530 | \n",
+ " 3.964931 | \n",
+ " 3.857324 | \n",
+ " 3.822143 | \n",
+ " 3.966396 | \n",
+ " 3.646041 | \n",
+ " 3.969461 | \n",
+ " 3.744342 | \n",
+ " 12.608084 | \n",
+ " 12.630799 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 30016.277260 | \n",
+ " 37347.622538 | \n",
+ " 12.767833 | \n",
+ " 1128.126574 | \n",
+ " 1.588697 | \n",
+ " 1.552213 | \n",
+ " 1.575306 | \n",
+ " 1.374244 | \n",
+ " 1.236187 | \n",
+ " 1.191609 | \n",
+ " 1.142077 | \n",
+ " 1.076907 | \n",
+ " 1.127130 | \n",
+ " 1.175515 | \n",
+ " 1.099607 | \n",
+ " 1.158732 | \n",
+ " 1.091486 | \n",
+ " 1.142247 | \n",
+ " 35.382595 | \n",
+ " 35.962008 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 7.000000 | \n",
+ " 31.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 25679.000000 | \n",
+ " 33366.000000 | \n",
+ " 32.000000 | \n",
+ " 526.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 51695.000000 | \n",
+ " 65862.000000 | \n",
+ " 43.000000 | \n",
+ " 1250.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 77741.000000 | \n",
+ " 97691.000000 | \n",
+ " 51.000000 | \n",
+ " 2405.000000 | \n",
+ " 5.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 9.000000 | \n",
+ " 8.000000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 103900.000000 | \n",
+ " 129879.000000 | \n",
+ " 85.000000 | \n",
+ " 4983.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 1305.000000 | \n",
+ " 1280.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Unnamed: 0 | \n",
+ " id | \n",
+ " Age | \n",
+ " Flight Distance | \n",
+ " Inflight wifi service | \n",
+ " Departure/Arrival time convenient | \n",
+ " Ease of Online booking | \n",
+ " Gate location | \n",
+ " Food and drink | \n",
+ " Online boarding | \n",
+ " Seat comfort | \n",
+ " Inflight entertainment | \n",
+ " On-board service | \n",
+ " Leg room service | \n",
+ " Baggage handling | \n",
+ " Checkin service | \n",
+ " Inflight service | \n",
+ " Cleanliness | \n",
+ " Departure Delay in Minutes | \n",
+ " Arrival Delay in Minutes | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58879.000000 | \n",
+ " 58697.000000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 52075.578746 | \n",
+ " 64474.259176 | \n",
+ " 37.566688 | \n",
+ " 928.919971 | \n",
+ " 2.399633 | \n",
+ " 3.129112 | \n",
+ " 2.546850 | \n",
+ " 2.976121 | \n",
+ " 2.958050 | \n",
+ " 2.656125 | \n",
+ " 3.036295 | \n",
+ " 2.894156 | \n",
+ " 3.019158 | \n",
+ " 2.990812 | \n",
+ " 3.375991 | \n",
+ " 3.042952 | \n",
+ " 3.388814 | \n",
+ " 2.936123 | \n",
+ " 16.503728 | \n",
+ " 17.127536 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 29977.755481 | \n",
+ " 37546.516040 | \n",
+ " 16.459825 | \n",
+ " 790.452308 | \n",
+ " 0.964348 | \n",
+ " 1.500368 | \n",
+ " 1.205847 | \n",
+ " 1.198500 | \n",
+ " 1.346607 | \n",
+ " 1.145905 | \n",
+ " 1.303142 | \n",
+ " 1.323619 | \n",
+ " 1.285790 | \n",
+ " 1.303159 | \n",
+ " 1.176978 | \n",
+ " 1.281158 | \n",
+ " 1.175603 | \n",
+ " 1.325955 | \n",
+ " 40.191886 | \n",
+ " 40.560248 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 7.000000 | \n",
+ " 31.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 26174.500000 | \n",
+ " 31835.500000 | \n",
+ " 25.000000 | \n",
+ " 372.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 2.000000 | \n",
+ " 3.000000 | \n",
+ " 2.000000 | \n",
+ " 3.000000 | \n",
+ " 2.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 52130.000000 | \n",
+ " 63970.000000 | \n",
+ " 36.000000 | \n",
+ " 671.000000 | \n",
+ " 2.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 78063.500000 | \n",
+ " 97144.500000 | \n",
+ " 50.000000 | \n",
+ " 1158.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 3.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 4.000000 | \n",
+ " 15.000000 | \n",
+ " 17.000000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 103903.000000 | \n",
+ " 129880.000000 | \n",
+ " 85.000000 | \n",
+ " 4983.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 5.000000 | \n",
+ " 1592.000000 | \n",
+ " 1584.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Unnamed: 0 | \n",
+ " id | \n",
+ " Gender | \n",
+ " Customer Type | \n",
+ " Age | \n",
+ " Type of Travel | \n",
+ " Class | \n",
+ " Flight Distance | \n",
+ " Inflight wifi service | \n",
+ " Departure/Arrival time convenient | \n",
+ " ... | \n",
+ " Inflight entertainment | \n",
+ " On-board service | \n",
+ " Leg room service | \n",
+ " Baggage handling | \n",
+ " Checkin service | \n",
+ " Inflight service | \n",
+ " Cleanliness | \n",
+ " Departure Delay in Minutes | \n",
+ " Arrival Delay in Minutes | \n",
+ " satisfaction | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2 | \n",
+ " 2 | \n",
+ " 110028 | \n",
+ " Female | \n",
+ " Loyal Customer | \n",
+ " 26 | \n",
+ " Business travel | \n",
+ " Business | \n",
+ " 1142 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " ... | \n",
+ " 5 | \n",
+ " 4 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 0.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 4 | \n",
+ " 119299 | \n",
+ " Male | \n",
+ " Loyal Customer | \n",
+ " 61 | \n",
+ " Business travel | \n",
+ " Business | \n",
+ " 214 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " ... | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 0.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 7 | \n",
+ " 96462 | \n",
+ " Female | \n",
+ " Loyal Customer | \n",
+ " 52 | \n",
+ " Business travel | \n",
+ " Business | \n",
+ " 2035 | \n",
+ " 4 | \n",
+ " 3 | \n",
+ " ... | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 0.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 13 | \n",
+ " 83502 | \n",
+ " Male | \n",
+ " Loyal Customer | \n",
+ " 33 | \n",
+ " Personal Travel | \n",
+ " Eco | \n",
+ " 946 | \n",
+ " 4 | \n",
+ " 2 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 0.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " 16 | \n",
+ " 71142 | \n",
+ " Female | \n",
+ " Loyal Customer | \n",
+ " 26 | \n",
+ " Business travel | \n",
+ " Business | \n",
+ " 2123 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 49 | \n",
+ " 51.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 103890 | \n",
+ " 103890 | \n",
+ " 80087 | \n",
+ " Female | \n",
+ " Loyal Customer | \n",
+ " 56 | \n",
+ " Business travel | \n",
+ " Eco Plus | \n",
+ " 550 | \n",
+ " 3 | \n",
+ " 5 | \n",
+ " ... | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 0.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | 103891 | \n",
+ " 103891 | \n",
+ " 83013 | \n",
+ " Male | \n",
+ " Loyal Customer | \n",
+ " 54 | \n",
+ " Business travel | \n",
+ " Business | \n",
+ " 1991 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 35 | \n",
+ " 31.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | 103894 | \n",
+ " 103894 | \n",
+ " 86549 | \n",
+ " Male | \n",
+ " Loyal Customer | \n",
+ " 26 | \n",
+ " Business travel | \n",
+ " Business | \n",
+ " 712 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 5 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 17 | \n",
+ " 26.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | 103897 | \n",
+ " 103897 | \n",
+ " 102203 | \n",
+ " Female | \n",
+ " Loyal Customer | \n",
+ " 60 | \n",
+ " Business travel | \n",
+ " Business | \n",
+ " 1599 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " ... | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 9 | \n",
+ " 7.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ " | 103900 | \n",
+ " 103900 | \n",
+ " 73097 | \n",
+ " Male | \n",
+ " Loyal Customer | \n",
+ " 49 | \n",
+ " Business travel | \n",
+ " Business | \n",
+ " 2347 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " ... | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 0.0 | \n",
+ " satisfied | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
45025 rows × 25 columns
\n",
+ "