f8x_crankhub_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fadde482-4a67-4080-9bc6-efb6a8f10a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Counts</th>\n",
       "      <th>Percentage of Whole</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-2015 | 6MT | Stock | No SCH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103</td>\n",
       "      <td>0.0647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-2015 | 6MT | Stock | SCH Failure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-2015 | 6MT | Modified 450-500 whp | No SCH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-2015 | 6MT | Modified 450-500 whp | SCH F...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-2015 | 6MT | Modified 500-550 whp | No SCH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Model  Unnamed: 1  Counts  \\\n",
       "0                   2014-2015 | 6MT | Stock | No SCH         NaN     103   \n",
       "1              2014-2015 | 6MT | Stock | SCH Failure         NaN       6   \n",
       "2    2014-2015 | 6MT | Modified 450-500 whp | No SCH         NaN      39   \n",
       "3  2014-2015 | 6MT | Modified 450-500 whp | SCH F...         NaN      10   \n",
       "4    2014-2015 | 6MT | Modified 500-550 whp | No SCH         NaN      20   \n",
       "\n",
       "   Percentage of Whole  \n",
       "0               0.0647  \n",
       "1               0.0038  \n",
       "2               0.0245  \n",
       "3               0.0063  \n",
       "4               0.0126  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the Excel file\n",
    "file_path = '/Users/dre/repos/BMW-F8x-Crankhub-Failure-Analysis/f8x_crankhub_data.xlsx'\n",
    "data = pd.read_excel(file_path)\n",
    "\n",
    "# Display the first few rows to inspect the data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c23e384-bac7-4789-a160-80b7d1680214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2014-2015 | 6MT | Stock | No SCH'\n",
      " '2014-2015 | 6MT | Stock | SCH Failure'\n",
      " '2014-2015 | 6MT | Modified 450-500 whp | No SCH'\n",
      " '2014-2015 | 6MT | Modified 450-500 whp | SCH Failure'\n",
      " '2014-2015 | 6MT | Modified 500-550 whp | No SCH'\n",
      " '2014-2015 | 6MT | Modified 500-550 whp | SCH Failure'\n",
      " '2014-2015 | 6MT | Modified 550+ whp | No SCH'\n",
      " '2014-2015 | 6MT | Modified 550+ whp | SCH Failure'\n",
      " '2014-2015 | DCT | Stock | No SCH'\n",
      " '2014-2015 | DCT | Stock | SCH Failure'\n",
      " '2014-2015 | DCT | Modified 450-500 whp | No SCH'\n",
      " '2014-2015 | DCT | Modified 450-500 whp | SCH Failure'\n",
      " '2014-2015 | DCT | Modified 500-550 whp | No SCH'\n",
      " '2014-2015 | DCT | Modified 500-550 whp | SCH Failure'\n",
      " '2014-2015 | DCT | Modified 550+ whp | No SCH'\n",
      " '2014-2015 | DCT | Modified 550+ whp | SCH Failure'\n",
      " '2016 | 6MT | Stock | No SCH' '2016 | 6MT | Stock | SCH Failure'\n",
      " '2016 | 6MT | Modified 450-500 whp | No SCH'\n",
      " '2016 | 6MT | Modified 450-500 whp | SCH Failure'\n",
      " '2016 | 6MT | Modified 500-550 whp | No SCH'\n",
      " '2016 | 6MT | Modified 500-550 whp | SCH Failure'\n",
      " '2016 | 6MT | Modified 550+ whp | No SCH'\n",
      " '2016 | 6MT | Modified 550+ whp | SCH Failure'\n",
      " '2016 | 6MT Comp Package | Stock | No SCH'\n",
      " '2016 | 6MT Comp Package | Stock | SCH Failure'\n",
      " '2016 | 6MT Comp Package | Modified 450-500 whp | No SCH'\n",
      " '2016 | 6MT Comp Package | Modified 450-500 whp | SCH Failure'\n",
      " '2016 | 6MT Comp Package | Modified 500-550 whp | No SCH'\n",
      " '2016 | 6MT Comp Package | Modified 500-550 whp | SCH Failure'\n",
      " '2016 | 6MT Comp Package | Modified 550+ whp | No SCH'\n",
      " '2016 | 6MT Comp Package | Modified 550+ whp | SCH Failure'\n",
      " '2016 | DCT | Stock | No SCH' '2016 | DCT | Stock | SCH Failure'\n",
      " '2016 | DCT | Modified 450-500 whp | No SCH'\n",
      " '2016 | DCT | Modified 450-500 whp | SCH Failure'\n",
      " '2016 | DCT | Modified 500-550 whp | No SCH'\n",
      " '2016 | DCT | Modified 500-550 whp | SCH Failure'\n",
      " '2016 | DCT | Modified 550+ whp | No SCH'\n",
      " '2016 | DCT | Modified 550+ whp | SCH Failure'\n",
      " '2016 | DCT Comp Package | Stock | No SCH'\n",
      " '2016 | DCT Comp Package | Stock | SCH Failure'\n",
      " '2016 | DCT Comp Package | Modified 450-500 whp | No SCH'\n",
      " '2016 | DCT Comp Package | Modified 450-500 whp | SCH Failure'\n",
      " '2016 | DCT Comp Package | Modified 500-550 whp | No SCH'\n",
      " '2016 | DCT Comp Package | Modified 500-550 whp | SCH Failure'\n",
      " '2016 | DCT Comp Package | Modified 550+ whp | No SCH'\n",
      " '2016 | DCT Comp Package | Modified 550+ whp | SCH Failure'\n",
      " '2017 | 6MT | Stock | No SCH' '2017 | 6MT | Stock | SCH Failure'\n",
      " '2017 | 6MT | Modified 450-500 whp | No SCH'\n",
      " '2017 | 6MT | Modified 450-500 whp | SCH Failure'\n",
      " '2017 | 6MT | Modified 500-550 whp | No SCH'\n",
      " '2017 | 6MT | Modified 500-550 whp | SCH Failure'\n",
      " '2017 | 6MT | Modified 550+ whp | No SCH'\n",
      " '2017 | 6MT | Modified 550+ whp | SCH Failure'\n",
      " '2017 | 6MT Comp Package | Stock | No SCH'\n",
      " '2017 | 6MT Comp Package | Stock | SCH Failure'\n",
      " '2017 | 6MT Comp Package | Modified 450-500 whp | No SCH'\n",
      " '2017 | 6MT Comp Package | Modified 450-500 whp | SCH Failure'\n",
      " '2017 | 6MT Comp Package | Modified 500-550 whp | No SCH'\n",
      " '2017 | 6MT Comp Package | Modified 500-550 whp | SCH Failure'\n",
      " '2017 | 6MT Comp Package | Modified 550+ whp | No SCH'\n",
      " '2017 | 6MT Comp Package | Modified 550+ whp | SCH Failure'\n",
      " '2017 | DCT | Stock | No SCH' '2017 | DCT | Stock | SCH Failure'\n",
      " '2017 | DCT | Modified 450-500 whp | No SCH'\n",
      " '2017 | DCT | Modified 450-500 whp | SCH Failure'\n",
      " '2017 | DCT | Modified 500-550 whp | No SCH'\n",
      " '2017 | DCT | Modified 500-550 whp | SCH Failure'\n",
      " '2017 | DCT | Modified 550+ whp | No SCH'\n",
      " '2017 | DCT | Modified 550+ whp | SCH Failure'\n",
      " '2017 | DCT Comp Package | Stock | No SCH'\n",
      " '2017 | DCT Comp Package | Stock | SCH Failure'\n",
      " '2017 | DCT Comp Package | Modified 450-500 whp | No SCH'\n",
      " '2017 | DCT Comp Package | Modified 450-500 whp | SCH Failure'\n",
      " '2017 | DCT Comp Package | Modified 500-550 whp | No SCH'\n",
      " '2017 | DCT Comp Package | Modified 500-550 whp | SCH Failure'\n",
      " '2017 | DCT Comp Package | Modified 550+ whp | No SCH'\n",
      " '2017 | DCT Comp Package | Modified 550+ whp | SCH Failure'\n",
      " '2018+ | 6MT | Stock | No SCH' '2018+ | 6MT | Stock | SCH Failure'\n",
      " '2018+ | 6MT | Modified 450-500 whp | No SCH'\n",
      " '2018+ | 6MT | Modified 450-500 whp | SCH Failure'\n",
      " '2018+ | 6MT | Modified 500-550 whp | No SCH'\n",
      " '2018+ | 6MT | Modified 500-550 whp | SCH Failure'\n",
      " '2018+ | 6MT | Modified 550+ whp | No SCH'\n",
      " '2018+ | 6MT | Modified 550+ whp | SCH Failure'\n",
      " '2018+ | 6MT Comp Package | Stock | No SCH'\n",
      " '2018+ | 6MT Comp Package | Stock | SCH Failure'\n",
      " '2018+ | 6MT Comp Package | Modified 450-500 whp | No SCH'\n",
      " '2018+ | 6MT Comp Package | Modified 450-500 whp | SCH Failure'\n",
      " '2018+ | 6MT Comp Package | Modified 500-550 whp | No SCH'\n",
      " '2018+ | 6MT Comp Package | Modified 500-550 whp | SCH Failure'\n",
      " '2018+ | 6MT Comp Package | Modified 550+ whp | No SCH'\n",
      " '2018+ | 6MT Comp Package | Modified 550+ whp | SCH Failure'\n",
      " '2018+ | DCT | Stock | No SCH' '2018+ | DCT | Stock | SCH Failure'\n",
      " '2018+ | DCT | Modified 450-500 whp | No SCH'\n",
      " '2018+ | DCT | Modified 450-500 whp | SCH Failure'\n",
      " '2018+ | DCT | Modified 500-550 whp | No SCH'\n",
      " '2018+ | DCT | Modified 500-550 whp | SCH Failure'\n",
      " '2018+ | DCT | Modified 550+ whp | No SCH'\n",
      " '2018+ | DCT | Modified 550+ whp | SCH Failure'\n",
      " '2018+ | DCT Comp Package | Stock | No SCH'\n",
      " '2018+ | DCT Comp Package | Stock | SCH Failure'\n",
      " '2018+ | DCT Comp Package | Modified 450-500 whp | No SCH'\n",
      " '2018+ | DCT Comp Package | Modified 450-500 whp | SCH Failure'\n",
      " '2018+ | DCT Comp Package | Modified 500-550 whp | No SCH'\n",
      " '2018+ | DCT Comp Package | Modified 500-550 whp | SCH Failure'\n",
      " '2018+ | DCT Comp Package | Modified 550+ whp | No SCH'\n",
      " '2018+ | DCT Comp Package | Modified 550+ whp | SCH Failure']\n",
      "['2014-2015' '2016' '2017' '2018+']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Drop the unnecessary column if it exists\n",
    "data = data.drop(columns=['Unnamed: 1'], errors='ignore')\n",
    "\n",
    "# Check unique values in the 'Model' column to understand its structure\n",
    "print(data['Model'].unique())\n",
    "\n",
    "# Split the 'Model' column into separate categories\n",
    "model_split = data['Model'].str.split(' \\| ', expand=True)\n",
    "model_split.columns = ['Year', 'Transmission', 'Modification', 'SCH_Status']\n",
    "\n",
    "# Print unique values in 'Year' column to check for formats\n",
    "print(model_split['Year'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f6caac4-82d2-43e2-b7b6-ef681ceef545",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Counts  Percentage of Whole\n",
      "count  112.000000           112.000000\n",
      "mean    14.544643             0.009146\n",
      "std     26.399866             0.016592\n",
      "min      0.000000             0.000000\n",
      "25%      2.000000             0.001300\n",
      "50%      4.000000             0.002500\n",
      "75%     13.250000             0.008350\n",
      "max    151.000000             0.094900\n"
     ]
    }
   ],
   "source": [
    "# Display summary statistics of numeric columns\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43240731-25d8-4e7d-8142-e62d8a9edee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112 entries, 0 to 111\n",
      "Data columns (total 3 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Model                112 non-null    object \n",
      " 1   Counts               112 non-null    int64  \n",
      " 2   Percentage of Whole  112 non-null    float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 2.8+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ac3aaab-40b9-4f2a-9faf-ec11b262d4cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year Transmission          Modification   SCH_Status  Counts  \\\n",
      "0  2015          6MT                 Stock       No SCH     103   \n",
      "1  2015          6MT                 Stock  SCH Failure       6   \n",
      "2  2015          6MT  Modified 450-500 whp       No SCH      39   \n",
      "3  2015          6MT  Modified 450-500 whp  SCH Failure      10   \n",
      "4  2015          6MT  Modified 500-550 whp       No SCH      20   \n",
      "\n",
      "   Percentage of Whole  \n",
      "0               0.0647  \n",
      "1               0.0038  \n",
      "2               0.0245  \n",
      "3               0.0063  \n",
      "4               0.0126  \n",
      "Error: 'Model' column is missing in the DataFrame.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Define a function to clean and adjust 'Year' values\n",
    "def clean_year(year):\n",
    "    try:\n",
    "        year_str = str(year)\n",
    "        if '+' in year_str:\n",
    "            return 2019\n",
    "        if '-' in year_str:\n",
    "            return int(year_str.split('-')[-1])\n",
    "        return int(year_str)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "# Apply the function to clean 'Year' column and ensure it's an integer type\n",
    "model_split['Year'] = model_split['Year'].apply(clean_year)\n",
    "\n",
    "# Convert 'Year' column to integer type, handling None values\n",
    "model_split['Year'] = model_split['Year'].fillna(0).astype(int)\n",
    "\n",
    "# Combine the split columns with the original data\n",
    "cleaned_data = pd.concat([model_split, data[['Counts', 'Percentage of Whole']]], axis=1)\n",
    "\n",
    "# Display the cleaned and organized data\n",
    "print(cleaned_data.head())\n",
    "\n",
    "# Filter data to include only SCH Failure entries\n",
    "if 'Model' in cleaned_data.columns:\n",
    "    sch_failures = cleaned_data[cleaned_data['Model'].str.contains('SCH Failure', na=False)]\n",
    "    \n",
    "    # Ensure 'Counts' column is present for plotting\n",
    "    if 'Counts' in sch_failures.columns:\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        sns.barplot(x='Counts', y='Model', data=sch_failures)\n",
    "        plt.title('SCH Failures by Model')\n",
    "        plt.xlabel('Number of SCH Failures')\n",
    "        plt.ylabel('Model')\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(\"Error: 'Counts' column is missing in the filtered DataFrame.\")\n",
    "else:\n",
    "    print(\"Error: 'Model' column is missing in the DataFrame.\")\n",
    "\n",
    "# Filter data for SCH Failure entries\n",
    "sch_failures = cleaned_data[cleaned_data['SCH_Status'].str.contains('SCH Failure', na=False)]\n",
    "\n",
    "# Group by Year and sum Counts\n",
    "yearly_trends = sch_failures.groupby('Year')['Counts'].sum().reset_index()\n",
    "\n",
    "# Ensure Year column only contains the years of interest\n",
    "years_of_interest = [2015, 2016, 2017, 2019]\n",
    "yearly_trends = yearly_trends[yearly_trends['Year'].isin(years_of_interest)]\n",
    "\n",
    "# Convert Year to categorical type with specific order\n",
    "yearly_trends['Year'] = pd.Categorical(yearly_trends['Year'], categories=years_of_interest, ordered=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c35fad1f-5cfa-41ac-82b8-e0be4575b10e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/p4/5cc14tlj2rvd2dd0x8bjh8hm0000gn/T/ipykernel_22281/2963141583.py:16: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(x='Transmission', y='Counts', data=transmission_trends, ci=None)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot the trend over years\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='Year', y='Counts', data=yearly_trends, marker='o')\n",
    "plt.title('Trend of SCH Failures Over Years')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of SCH Failures')\n",
    "plt.xticks(ticks=years_of_interest, labels=years_of_interest)  # Set x-axis ticks to the specific years\n",
    "plt.grid(True)  # Add grid lines for better readability\n",
    "plt.show()\n",
    "\n",
    "# Group by Transmission and sum Counts\n",
    "transmission_trends = sch_failures.groupby('Transmission')['Counts'].sum().reset_index()\n",
    "\n",
    "# Plot SCH Failures by Transmission Type\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x='Transmission', y='Counts', data=transmission_trends, ci=None)\n",
    "plt.title('SCH Failures by Transmission Type')\n",
    "plt.xlabel('Transmission Type')\n",
    "plt.ylabel('Number of SCH Failures')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a304d71-1d3f-48b4-9a16-6cc761fdf002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Year      Transmission          Modification  Counts\n",
      "7   2015               DCT                 Stock     155\n",
      "19  2016               DCT                 Stock     115\n",
      "4   2015               DCT  Modified 450-500 whp     112\n",
      "3   2015               6MT                 Stock     109\n",
      "55  2019  DCT Comp Package                 Stock     108\n",
      "51  2019               DCT                 Stock      83\n",
      "47  2019  6MT Comp Package                 Stock      64\n",
      "11  2016               6MT                 Stock      60\n",
      "43  2019               6MT                 Stock      56\n",
      "5   2015               DCT  Modified 500-550 whp      54\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Determine the most common car combinations\n",
    "common_combinations = cleaned_data.groupby(['Year', 'Transmission', 'Modification']).agg({\n",
    "    'Counts': 'sum'\n",
    "}).reset_index()\n",
    "\n",
    "# Sort by the total counts in descending order\n",
    "common_combinations = common_combinations.sort_values(by='Counts', ascending=False)\n",
    "\n",
    "# Display the top 10 most common combinations as a table\n",
    "print(common_combinations.head(10))  # Display the top 10 most common combinations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c1906384-937a-4de8-b12a-6dce6296678d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Display the top 10 most common combinations\n",
    "top_10_combinations = common_combinations.head(10)\n",
    "\n",
    "# Create a bar chart\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "# Generate a bar plot\n",
    "bars = ax.barh(top_10_combinations.apply(lambda x: f\"{x['Year']} {x['Transmission']} {x['Modification']}\", axis=1),\n",
    "               top_10_combinations['Counts'],\n",
    "               color='skyblue')\n",
    "\n",
    "# Add labels and title\n",
    "ax.set_xlabel('Counts')\n",
    "ax.set_ylabel('Car Combinations')\n",
    "ax.set_title('Top 10 Most Common Car Combinations')\n",
    "\n",
    "# Add value labels on bars\n",
    "for bar in bars:\n",
    "    width = bar.get_width()\n",
    "    ax.text(width + 3, bar.get_y() + bar.get_height()/2,\n",
    "            f'{width}',\n",
    "            va='center')\n",
    "\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the highest counts on top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9e9e8adc-4910-4df9-9fdc-6d802d20e200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCH_Status            No SCH  SCH Failure  Total  SCH Failure Percentage\n",
      "Modification                                                            \n",
      "Modified 450-500 whp     311           47    358               13.128492\n",
      "Modified 500-550 whp     183           40    223               17.937220\n",
      "Modified 550+ whp         91           44    135               32.592593\n",
      "Stock                    883           30    913                3.285871\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Create a summary table for SCH failures by modification type\n",
    "summary_table = cleaned_data.groupby(['Modification', 'SCH_Status'])['Counts'].sum().unstack().fillna(0)\n",
    "\n",
    "# Calculate percentages for better comparison\n",
    "summary_table['Total'] = summary_table.sum(axis=1)\n",
    "summary_table['SCH Failure Percentage'] = (summary_table['SCH Failure'] / summary_table['Total']) * 100\n",
    "\n",
    "# Print the summary table\n",
    "print(summary_table)\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(x=summary_table.index, y='SCH Failure Percentage', data=summary_table.reset_index())\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.title('Percentage of SCH Failures by Car Modification')\n",
    "plt.xlabel('Car Modification')\n",
    "plt.ylabel('Percentage of SCH Failures')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c25c76d5-b579-4a2c-abe9-38f725483f00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Year', 'Transmission', 'Modification', 'SCH_Status', 'Counts',\n",
      "       'Percentage of Whole'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(cleaned_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "848d3e75-fb0d-4174-adfa-8158b25ffe0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    112.000000\n",
      "mean      14.544643\n",
      "std       26.399866\n",
      "min        0.000000\n",
      "25%        2.000000\n",
      "50%        4.000000\n",
      "75%       13.250000\n",
      "max      151.000000\n",
      "Name: Counts, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculate summary statistics\n",
    "summary_stats = data['Counts'].describe()\n",
    "print(summary_stats)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "98d03f05-0110-4a2f-8d65-b6830e5ac6cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset successfully exported to cleaned_dataset.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Specify the output file path\n",
    "output_file = 'cleaned_dataset.xlsx'\n",
    "\n",
    "# Export the DataFrame to an Excel file\n",
    "cleaned_data.to_excel(output_file, index=False)\n",
    "\n",
    "print(f\"Dataset successfully exported to {output_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1f0ae3e5-d8fc-4f80-b18d-4c98cf988ff8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of responses: 1629\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculate the total number of responses\n",
    "total_responses = cleaned_data['Counts'].sum()\n",
    "print(f\"Total number of responses: {total_responses}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4bcfcf34-36db-457a-b007-cb9e0dd8f028",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 20\u001b[0m\n\u001b[1;32m     17\u001b[0m X \u001b[38;5;241m=\u001b[39m sm\u001b[38;5;241m.\u001b[39madd_constant(X)\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# Fit the model\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43msm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mOLS\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mfit()\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# Print the summary\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28mprint\u001b[39m(model\u001b[38;5;241m.\u001b[39msummary())\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/regression/linear_model.py:924\u001b[0m, in \u001b[0;36mOLS.__init__\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    921\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWeights are not supported in OLS and will be ignored\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    922\u001b[0m            \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn exception will be raised in the next version.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    923\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(msg, ValueWarning)\n\u001b[0;32m--> 924\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    925\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mhasconst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhasconst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    926\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_keys:\n\u001b[1;32m    927\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_keys\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/regression/linear_model.py:749\u001b[0m, in \u001b[0;36mWLS.__init__\u001b[0;34m(self, endog, exog, weights, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    748\u001b[0m     weights \u001b[38;5;241m=\u001b[39m weights\u001b[38;5;241m.\u001b[39msqueeze()\n\u001b[0;32m--> 749\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    750\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhasconst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhasconst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    751\u001b[0m nobs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    752\u001b[0m weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/regression/linear_model.py:203\u001b[0m, in \u001b[0;36mRegressionModel.__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 203\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpinv_wexog: Float64Array \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    205\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_attr\u001b[38;5;241m.\u001b[39mextend([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpinv_wexog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwendog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwexog\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/base/model.py:270\u001b[0m, in \u001b[0;36mLikelihoodModel.__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 270\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    271\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitialize()\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/base/model.py:95\u001b[0m, in \u001b[0;36mModel.__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m missing \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmissing\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     94\u001b[0m hasconst \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhasconst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m---> 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhasconst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[43m                              \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk_constant \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mk_constant\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mexog\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/base/model.py:135\u001b[0m, in \u001b[0;36mModel._handle_data\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_handle_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, endog, exog, missing, hasconst, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 135\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mhandle_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhasconst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;66;03m# kwargs arrays could have changed, easier to just attach here\u001b[39;00m\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m kwargs:\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/base/data.py:675\u001b[0m, in \u001b[0;36mhandle_data\u001b[0;34m(endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m    672\u001b[0m     exog \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(exog)\n\u001b[1;32m    674\u001b[0m klass \u001b[38;5;241m=\u001b[39m handle_data_class_factory(endog, exog)\n\u001b[0;32m--> 675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mklass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhasconst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhasconst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m             \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/base/data.py:84\u001b[0m, in \u001b[0;36mModelData.__init__\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morig_endog \u001b[38;5;241m=\u001b[39m endog\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morig_exog \u001b[38;5;241m=\u001b[39m exog\n\u001b[0;32m---> 84\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendog, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexog \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_endog_exog\u001b[49m\u001b[43m(\u001b[49m\u001b[43mendog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexog\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconst_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     87\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk_constant \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/statsmodels/base/data.py:509\u001b[0m, in \u001b[0;36mPandasData._convert_endog_exog\u001b[0;34m(self, endog, exog)\u001b[0m\n\u001b[1;32m    507\u001b[0m exog \u001b[38;5;241m=\u001b[39m exog \u001b[38;5;28;01mif\u001b[39;00m exog \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(exog)\n\u001b[1;32m    508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m endog\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m exog \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m exog\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m--> 509\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPandas data cast to numpy dtype of object. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    510\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCheck input data with np.asarray(data).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m_convert_endog_exog(endog, exog)\n",
      "\u001b[0;31mValueError\u001b[0m: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data)."
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Assuming `cleaned_data` is your DataFrame\n",
    "\n",
    "# Filter for SCH_Status = SCH Failure\n",
    "filtered_data = cleaned_data[cleaned_data['SCH_Status'] == 'SCH Failure']\n",
    "\n",
    "# Convert categorical variables to dummy variables\n",
    "filtered_data_dummies = pd.get_dummies(filtered_data, columns=['Transmission', 'Modification'], drop_first=True)\n",
    "\n",
    "# Prepare features and target variable\n",
    "X = filtered_data_dummies[['Year'] + [col for col in filtered_data_dummies.columns if col.startswith('Transmission') or col.startswith('Modification')]]\n",
    "y = filtered_data_dummies['Counts']\n",
    "\n",
    "# Add a constant to the features matrix (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Fit the model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Print the summary\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "70bc1e91-5c45-49ea-9e13-996f08b9afb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "const                                float64\n",
      "Year                                   int64\n",
      "Transmission_6MT Comp Package           bool\n",
      "Transmission_DCT                        bool\n",
      "Transmission_DCT Comp Package           bool\n",
      "Modification_Modified 500-550 whp       bool\n",
      "Modification_Modified 550+ whp          bool\n",
      "Modification_Stock                      bool\n",
      "dtype: object\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "print(X.dtypes)\n",
    "print(y.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dba720c1-d054-4181-94ea-4aa8736ba55b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Counts   R-squared:                       0.118\n",
      "Model:                            OLS   Adj. R-squared:                 -0.011\n",
      "Method:                 Least Squares   F-statistic:                    0.9173\n",
      "Date:                Mon, 30 Sep 2024   Prob (F-statistic):              0.502\n",
      "Time:                        08:23:43   Log-Likelihood:                -135.40\n",
      "No. Observations:                  56   AIC:                             286.8\n",
      "Df Residuals:                      48   BIC:                             303.0\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================================\n",
      "                                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "const                               208.7894    570.931      0.366      0.716    -939.144    1356.722\n",
      "Year                                 -0.1017      0.283     -0.359      0.721      -0.671       0.467\n",
      "Modification_Modified 500-550 whp    -0.5000      1.109     -0.451      0.654      -2.729       1.729\n",
      "Modification_Modified 550+ whp       -0.2143      1.109     -0.193      0.848      -2.443       2.015\n",
      "Modification_Stock                   -1.2143      1.109     -1.095      0.279      -3.443       1.015\n",
      "Transmission_6MT Comp Package        -1.7948      1.132     -1.585      0.119      -4.071       0.481\n",
      "Transmission_DCT                      0.5625      1.037      0.542      0.590      -1.522       2.647\n",
      "Transmission_DCT Comp Package        -0.2948      1.132     -0.260      0.796      -2.571       1.981\n",
      "==============================================================================\n",
      "Omnibus:                       27.177   Durbin-Watson:                   0.789\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               52.311\n",
      "Skew:                           1.535   Prob(JB):                     4.37e-12\n",
      "Kurtosis:                       6.604   Cond. No.                     2.94e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.94e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Assuming `cleaned_data` is your DataFrame\n",
    "\n",
    "# Filter for SCH_Status = SCH Failure\n",
    "filtered_data = cleaned_data[cleaned_data['SCH_Status'] == 'SCH Failure']\n",
    "\n",
    "# Convert categorical variables to dummy variables\n",
    "filtered_data_dummies = pd.get_dummies(filtered_data, columns=['Modification', 'Transmission'], drop_first=True)\n",
    "\n",
    "# Prepare the features and target variable\n",
    "X = filtered_data_dummies[['Year'] + [col for col in filtered_data_dummies.columns if col.startswith('Modification') or col.startswith('Transmission')]]\n",
    "y = pd.to_numeric(filtered_data_dummies['Counts'], errors='coerce')\n",
    "\n",
    "# Ensure all columns in X are numeric\n",
    "X = X.astype(int)\n",
    "\n",
    "# Add a constant to the features matrix (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Drop NaN values from X and y\n",
    "X = X.dropna()\n",
    "y = y[X.index]  # Ensure y aligns with X after dropping NaN values\n",
    "\n",
    "# Fit the model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Print the summary\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a7600af8-2003-400c-8df5-6c4e359f010b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                 Counts   No. Observations:                   56\n",
      "Model:                            GLM   Df Residuals:                       48\n",
      "Model Family:                 Poisson   Df Model:                            7\n",
      "Link Function:                    Log   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -130.79\n",
      "Date:                Mon, 30 Sep 2024   Deviance:                       128.19\n",
      "Time:                        08:23:46   Pearson chi2:                     127.\n",
      "No. Iterations:                     5   Pseudo R-squ. (CS):             0.3186\n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================================\n",
      "                                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------\n",
      "const                                70.7398    113.900      0.621      0.535    -152.501     293.980\n",
      "Year                                 -0.0344      0.056     -0.610      0.542      -0.145       0.076\n",
      "Modification_Modified 500-550 whp    -0.1613      0.215     -0.750      0.453      -0.583       0.260\n",
      "Modification_Modified 550+ whp       -0.0660      0.210     -0.314      0.753      -0.477       0.345\n",
      "Modification_Stock                   -0.4490      0.234     -1.921      0.055      -0.907       0.009\n",
      "Transmission_6MT Comp Package        -0.8511      0.289     -2.949      0.003      -1.417      -0.286\n",
      "Transmission_DCT                      0.1625      0.190      0.853      0.393      -0.211       0.536\n",
      "Transmission_DCT Comp Package        -0.0973      0.224     -0.434      0.664      -0.536       0.342\n",
      "=====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Fit Poisson regression\n",
    "poisson_model = sm.GLM(y, sm.add_constant(X), family=sm.families.Poisson()).fit()\n",
    "print(poisson_model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "40056604-9a09-4b85-81ca-e4d45c014dc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Feature  Coefficient\n",
      "0       1     0.000000\n",
      "1    Year -4761.415440\n",
      "2  Year^2     1.180147\n",
      "R-squared: 0.4716563261127299\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Assuming `cleaned_data` is your DataFrame\n",
    "\n",
    "# Filter for SCH_Status = SCH Failure\n",
    "filtered_data = cleaned_data[cleaned_data['SCH_Status'] == 'SCH Failure']\n",
    "\n",
    "# Prepare the features and target variable\n",
    "X = filtered_data[['Year']]\n",
    "y = filtered_data['Counts']\n",
    "\n",
    "# Convert target variable to numeric\n",
    "y = pd.to_numeric(y, errors='coerce')\n",
    "\n",
    "# Create polynomial features\n",
    "poly = PolynomialFeatures(degree=2)  # Change the degree as needed\n",
    "X_poly = poly.fit_transform(X)\n",
    "\n",
    "# Fit the model\n",
    "model = LinearRegression()\n",
    "model.fit(X_poly, y)\n",
    "\n",
    "# Create a DataFrame for the results\n",
    "results = pd.DataFrame({'Feature': poly.get_feature_names_out(), 'Coefficient': model.coef_})\n",
    "\n",
    "# Print the coefficients\n",
    "print(results)\n",
    "\n",
    "# Summary statistics\n",
    "print(f\"R-squared: {model.score(X_poly, y)}\")\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "87f0a0cc-3af1-49fc-9c10-69fc0fc24e4a",
   "metadata": {},
   "source": [
    "# Coefficients\n",
    "## Intercept (1): \n",
    "The constant term is zero, which is typical when predicting counts.\n",
    "Year: The negative coefficient (-4761.42) suggests that as the year increases, the count of SCH failures decreases. This could imply that newer models are less likely to experience issues.\n",
    "Year^2: The positive coefficient (1.18) indicates a quadratic relationship. This suggests that the effect of the year on counts is not linear; rather, it might start decreasing and then increase again after a certain point.\n",
    "## R-squared Value\n",
    "An R-squared of 0.471 means that approximately 47.2% of the variance in your target variable (counts) is explained by the model. While this isn't extremely high, it does indicate some level of predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e8edc23-b904-4c2f-8e2b-1472cf63679e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-statistic: -2.935800384677399, P-value: 0.00645855492208009\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "# Assuming cleaned_data is your DataFrame\n",
    "# Create a binary column indicating if the car is modified or stock\n",
    "cleaned_data['Is_Modified'] = cleaned_data['Modification'].apply(lambda x: 1 if x != 'Stock' else 0)\n",
    "\n",
    "# Separate the groups\n",
    "modified_data = cleaned_data[cleaned_data['Is_Modified'] == 1]['Counts']\n",
    "stock_data = cleaned_data[cleaned_data['Is_Modified'] == 0]['Counts']\n",
    "\n",
    "# Perform the t-test\n",
    "t_stat, p_value = stats.ttest_ind(modified_data, stock_data, equal_var=False)  # Use unequal variance\n",
    "\n",
    "print(f'T-statistic: {t_stat}, P-value: {p_value}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a342134-8736-42c1-ab68-7188cb557542",
   "metadata": {},
   "source": [
    "### Leverages python pandas to dump grouped and organized data back to the original raw answer dataset gathered fron the poll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b446fd04-dd88-421c-8e57-e42fd2fb3b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Modification</th>\n",
       "      <th>SCH_Status</th>\n",
       "      <th>Counts</th>\n",
       "      <th>Percentage of Whole</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Stock</td>\n",
       "      <td>No SCH</td>\n",
       "      <td>103</td>\n",
       "      <td>0.0647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Stock</td>\n",
       "      <td>SCH Failure</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Modified 450-500 whp</td>\n",
       "      <td>No SCH</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Modified 450-500 whp</td>\n",
       "      <td>SCH Failure</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Modified 500-550 whp</td>\n",
       "      <td>No SCH</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Transmission          Modification   SCH_Status  Counts  \\\n",
       "0  2015          6MT                 Stock       No SCH     103   \n",
       "1  2015          6MT                 Stock  SCH Failure       6   \n",
       "2  2015          6MT  Modified 450-500 whp       No SCH      39   \n",
       "3  2015          6MT  Modified 450-500 whp  SCH Failure      10   \n",
       "4  2015          6MT  Modified 500-550 whp       No SCH      20   \n",
       "\n",
       "   Percentage of Whole  \n",
       "0               0.0647  \n",
       "1               0.0038  \n",
       "2               0.0245  \n",
       "3               0.0063  \n",
       "4               0.0126  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Excel file\n",
    "file_path = '/Users/dre/repos/BMW-F8x-Crankhub-Failure-Analysis/cleaned_dataset.xlsx'\n",
    "df = pd.read_excel(file_path)\n",
    "\n",
    "# Display the first few rows to inspect the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "775b0c55-bb46-42d8-817b-9806840d5f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Modification</th>\n",
       "      <th>SCH_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Stock</td>\n",
       "      <td>No SCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Stock</td>\n",
       "      <td>No SCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Stock</td>\n",
       "      <td>No SCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Stock</td>\n",
       "      <td>No SCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>6MT</td>\n",
       "      <td>Stock</td>\n",
       "      <td>No SCH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Transmission Modification SCH_Status\n",
       "0  2015          6MT        Stock     No SCH\n",
       "1  2015          6MT        Stock     No SCH\n",
       "2  2015          6MT        Stock     No SCH\n",
       "3  2015          6MT        Stock     No SCH\n",
       "4  2015          6MT        Stock     No SCH"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expand the dataset by replicating each row based on the 'Counts' column\n",
    "df_raw = df.loc[df.index.repeat(df['Counts'])].drop(columns=['Counts', 'Percentage of Whole'])\n",
    "\n",
    "# Reset index to clean up\n",
    "df_raw.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Preview the transformed raw dataset\n",
    "df_raw.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "59f94ac1-2018-41cd-ba5c-6d5321f550a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/dre/repos/BMW-F8x-Crankhub-Failure-Analysis/raw_dataset.xlsx'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the transformed raw data to a new Excel file\n",
    "raw_file_path = '/Users/dre/repos/BMW-F8x-Crankhub-Failure-Analysis/raw_dataset.xlsx'\n",
    "df_raw.to_excel(raw_file_path, index=False)\n",
    "\n",
    "raw_file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77c71e0d-209c-4b03-b7bd-b7c5cde7ca34",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}