Performing Descriptive Statistics with Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2dc1d71",
   "metadata": {},
   "source": [
    "# Q1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708e2f08",
   "metadata": {},
   "source": [
    "The mean price is higher than the median, which suggests a right-skewed distribution. This means there are some items that are significantly more expensive, pulling the average up. A high standard deviation indicates a wide variation in the prices of the food items.\n",
    "\n",
    "To better understand this, let's plot a hypothetical normal distribution with these statistics using Python. We'll assume the prices are normally distributed for this illustration, although in reality, food item prices might not follow a perfect normal distribution. This visualization will help us see the spread and central tendency of the prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "545fb037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "\n",
    "mean = 250\n",
    "std_dev = 47\n",
    "median = 202\n",
    "\n",
    "prices = np.linspace(mean - 3*std_dev, mean + 3*std_dev, 1000)\n",
    "\n",
    "pdf = stats.norm.pdf(prices, mean, std_dev)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(prices, pdf, label='Normal Distribution', color='blue')\n",
    "plt.axvline(x=mean, color='red', linestyle='--', label=f'Mean (Rs {mean})')\n",
    "plt.axvline(x=median, color='green', linestyle='--', label=f'Median (Rs {median})')\n",
    "\n",
    "plt.title('Distribution of Food Item Prices in the Restaurant')\n",
    "plt.xlabel('Price (Rs)')\n",
    "plt.ylabel('Probability Density')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435cc975",
   "metadata": {},
   "source": [
    "The plotted distribution gives us a visual representation of the food item prices in the restaurant. As observed:\n",
    "\n",
    "The red dashed line represents the mean (average) price of Rs 250.00.\n",
    "The green dashed line represents the median price, which is around Rs 202.00.\n",
    "The mean being higher than the median indicates a right-skewed distribution, suggesting that there are some more expensive items that increase the average price.\n",
    "\n",
    "The decision to become a regular customer at this restaurant would depend on several factors:\n",
    "\n",
    "Budget and Preferences: If your budget aligns with the median price rather than the mean, you might find the restaurant reasonably affordable since the median represents the middle value of the price range.\n",
    "\n",
    "Variety and Price Range: The high standard deviation (Rs 47.00) indicates a wide range of prices. If you prefer variety and don't mind occasionally splurging, this could be suitable.\n",
    "\n",
    "Value for Money: It's also important to consider the quality and portion sizes of the meals, which could justify higher prices.\n",
    "\n",
    "Personal Dining Habits: If you dine out frequently and prefer restaurants within a specific budget, you should consider how these prices fit into your regular dining budget.\n",
    "\n",
    "Ultimately, the decision to become a regular customer would be based on how well the restaurant's offerings align with your personal preferences and budget."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e619559",
   "metadata": {},
   "source": [
    "# Q2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423bc097",
   "metadata": {},
   "source": [
    "The mean and median are quite close to each other, and the standard deviation is not excessively large in comparison to the mean. This suggests that the house sizes are fairly consistent across the locality, with most houses being close to the average size.\n",
    "\n",
    "The similarity between the mean and median indicates that the distribution of house sizes is likely symmetrical, or close to normal, without significant skewness.\n",
    "\n",
    "To determine whether the houses are considered \"bigger\" or \"smaller,\" we need a point of reference or an average house size to compare against. Without a specific benchmark or average to compare to, it's difficult to categorically say whether these houses are bigger or smaller. However, if we assume that the average house size in the general region or country is lower than 2224 square feet, then we could infer that houses in this locality are relatively larger. Conversely, if the average house size is generally larger, then these would be considered smaller.\n",
    "\n",
    "We can create a hypothetical normal distribution plot to visualize the spread of house sizes in this locality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74742fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_size = 2224  # in square feet\n",
    "std_dev_size = 248  # in square feet\n",
    "median_size = 2220  # in square feet\n",
    "\n",
    "house_sizes = np.linspace(mean_size - 3*std_dev_size, mean_size + 3*std_dev_size, 1000)\n",
    "\n",
    "pdf_sizes = stats.norm.pdf(house_sizes, mean_size, std_dev_size)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(house_sizes, pdf_sizes, label='Normal Distribution', color='blue')\n",
    "plt.axvline(x=mean_size, color='red', linestyle='--', label=f'Mean (2224 sq ft)')\n",
    "plt.axvline(x=median_size, color='green', linestyle='--', label=f'Median (2220 sq ft)')\n",
    "\n",
    "plt.title('Distribution of House Sizes in the Locality')\n",
    "plt.xlabel('House Size (square feet)')\n",
    "plt.ylabel('Probability Density')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0217614a",
   "metadata": {},
   "source": [
    "The red dashed line indicates the mean size of houses, which is 2224 square feet.\n",
    "The green dashed line represents the median size, close to the mean at 2220 square feet.\n",
    "The closeness of the mean and median, along with a standard deviation of 248 square feet, suggests that the distribution of house sizes is fairly symmetrical and concentrated around the average size. This implies a consistent size range for most houses in the locality.\n",
    "\n",
    "Without a broader context or benchmark for comparison, it's difficult to definitively say if these houses are larger or smaller compared to a general average. However, the consistency in size indicates that the locality has a fairly uniform range of house sizes. If you have a specific average size for comparison (such as a regional or national average), we could make a more direct comparison to determine if these houses are relatively larger or smaller."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac7f8ba",
   "metadata": {},
   "source": [
    "# Q3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ace2cf",
   "metadata": {},
   "source": [
    "To determine which locality (X or Y) might be a better investment based on house prices, we can conduct basic statistical analysis on the provided data. We'll look at measures such as mean (average), median, and standard deviation for each locality. These measures can provide insights into the central tendency and variability of house prices in each locality.\n",
    "\n",
    "Data:\n",
    "\n",
    "Locality X: [2.00 L, 10.00 L, 8.00 L, 12.00 L]\n",
    "Locality Y: [2.5 L, 9.50 L, 8.15 L, 12.50 L]\n",
    "Let's calculate the following for each locality:\n",
    "\n",
    "Mean (Average): The average house price.\n",
    "Median: The middle value in the sorted list of house prices.\n",
    "Standard Deviation: A measure of the amount of variation or dispersion of house prices.\n",
    "After computing these, we can compare the localities. Typically, a lower standard deviation might indicate a more stable investment as the prices don't vary widely, while the mean and median can give us an idea of the general price level in each locality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f02eb82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.0, 9.0, 4.320493798938574, 8.1625, 8.825, 4.189943317039027)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_X = [2.00, 10.00, 8.00, 12.00]  # Locality X\n",
    "prices_Y = [2.5, 9.50, 8.15, 12.50]    # Locality Y\n",
    "\n",
    "mean_X = np.mean(prices_X)\n",
    "median_X = np.median(prices_X)\n",
    "std_dev_X = np.std(prices_X, ddof=1)\n",
    "\n",
    "mean_Y = np.mean(prices_Y)\n",
    "median_Y = np.median(prices_Y)\n",
    "std_dev_Y = np.std(prices_Y, ddof=1)\n",
    "\n",
    "mean_X, median_X, std_dev_X, mean_Y, median_Y, std_dev_Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41408e8",
   "metadata": {},
   "source": [
    "Based on the calculations:\n",
    "\n",
    "Locality X:\n",
    "\n",
    "Mean (Average) Price: 8.00 L\n",
    "Median Price: 9.00 L\n",
    "Standard Deviation: 4.32 L\n",
    "Locality Y:\n",
    "\n",
    "Mean (Average) Price: 8.1625 L\n",
    "Median Price: 8.825 L\n",
    "Standard Deviation: 4.19 L\n",
    "Analysis:\n",
    "\n",
    "Mean Price: Locality Y has a slightly higher average price compared to Locality X.\n",
    "Median Price: The median price is also slightly higher in Locality Y.\n",
    "Standard Deviation: Locality Y has a marginally lower standard deviation, indicating less variability in house prices compared to Locality X.\n",
    "Investment Decision:\n",
    "\n",
    "If you prioritize stability (lower variability), Locality Y seems marginally better as it has a slightly lower standard deviation.\n",
    "However, the difference in standard deviation is not substantial. Both localities show similar levels of price variability.\n",
    "The slightly higher average and median prices in Locality Y might indicate a higher demand or better amenities, but this is speculative without additional context.\n",
    "Ultimately, the decision should also consider other factors such as location, future development potential, rental yields, and personal investment goals. The statistical analysis provides just one aspect of the overall investment decision."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3d48a2",
   "metadata": {},
   "source": [
    "# Q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "54a3efb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('35 - 45', '55 - 65', 19, '35 - 45', '35 - 45')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_intervals = [\"5 - 15\", \"15 - 25\", \"25 - 35\", \"35 - 45\", \"45 - 55\", \"55 - 65\"]\n",
    "patients = [6, 11, 21, 23, 14, 5]\n",
    "total_patients = sum(patients)\n",
    "\n",
    "highest_freq_interval = age_intervals[patients.index(max(patients))]\n",
    "\n",
    "least_affected_age_group = age_intervals[patients.index(min(patients))]\n",
    "\n",
    "patients_45_and_above = sum(patients[age_intervals.index(\"45 - 55\"):])\n",
    "\n",
    "modal_class_interval = highest_freq_interval  # Same as the highest frequency class interval\n",
    "\n",
    "cumulative_patients = np.cumsum(patients)\n",
    "half_patients = total_patients / 2\n",
    "median_class_index = np.where(cumulative_patients >= half_patients)[0][0]\n",
    "median_class_interval = age_intervals[median_class_index]\n",
    "\n",
    "highest_freq_interval, least_affected_age_group, patients_45_and_above, modal_class_interval, median_class_interval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c64a0b",
   "metadata": {},
   "source": [
    "Based on the calculations, here are the answers to each of the questions:\n",
    "\n",
    "a. Highest Frequency Class Interval: The age group with the highest frequency is 35 - 45 years.\n",
    "\n",
    "b. Least Affected Age Group: The age group affected the least is 55 - 65 years.\n",
    "\n",
    "c. Patients of Age 45 Years and Above: There were 19 patients admitted who were aged 45 years and above.\n",
    "\n",
    "d. Modal Class Interval: The modal class interval, i.e., the class interval with the highest frequency, is 35 - 45 years.\n",
    "\n",
    "e. Median Class Interval: The median class interval of age, where the cumulative frequency reaches half of the total number of cases, is also 35 - 45 years."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ceb7e8",
   "metadata": {},
   "source": [
    "# Q5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6656d238",
   "metadata": {},
   "source": [
    "To compute the average return on investment (ROI) over these years, considering the fluctuating nature of returns, we can use the geometric mean. The geometric mean is more appropriate than the arithmetic mean in this context because it takes into account the compounding effect over time, which is crucial for investment returns.\n",
    "\n",
    "\n",
    "For calculating the geometric mean of investment returns, we need to convert the percentage returns into their decimal forms and consider them as factors of growth (or decline) from the initial value. For example, a 36% return is represented as \n",
    "1+0.36 = 1.36\n",
    "1+0.36=1.36, and a -48% return is \n",
    "1 − 0.48 = 0.52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9deabcfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4282599080668423"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns for each year in decimal form (e.g., 36% as 1.36, -48% as 0.52, etc.)\n",
    "returns = [1.36, 1.23, 0.52, 0.70, 1.15, 1.31]\n",
    "\n",
    "# Calculating the geometric mean\n",
    "geometric_mean = np.prod(returns) ** (1/len(returns))\n",
    "geometric_mean_percentage = (geometric_mean - 1) * 100  # Converting back to percentage\n",
    "geometric_mean_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cef24ec",
   "metadata": {},
   "source": [
    "The average return on investment, calculated using the geometric mean, is approximately −1.43. This indicates that, on average, there has been a slight decrease in the value of the investment each year when considering the compounded effect of the yearly returns. Also, geometric mean is particularly sensitive to years with large negative returns (like the -48% in 2017). Even with positive returns in other years, such significant losses can greatly affect the overall average, as seen in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5528dc",
   "metadata": {},
   "source": [
    "# Q6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4d80d71",
   "metadata": {},
   "source": [
    "Measuring the height of all men on Earth is a massive and impractical task due to the sheer number of individuals and the logistical, financial, and ethical challenges involved. However, the objective can be effectively addressed using statistical sampling and inferential statistics. Here's a strategy to estimate the average height:\n",
    "\n",
    "1) Define the Population: Clearly define the population under study, which in this case is all men on Earth.\n",
    "\n",
    "2) Choose a Sampling Method: Since it's impossible to measure everyone, a sampling method is used. A good approach would be stratified random sampling. This involves dividing the population into subgroups (strata) based on factors that might affect height, such as geographic region, ethnicity, age group, etc. Then, randomly sample individuals from each stratum.\n",
    "\n",
    "3) Determine Sample Size: Calculate the required sample size to achieve a desired level of accuracy and confidence. Larger sample sizes yield more accurate results but require more resources. There are statistical formulas to determine the optimal sample size based on the population size, expected variability in height, and the desired confidence level.\n",
    "\n",
    "4) Data Collection: Collect height data from the chosen samples. This can be done through surveys, health databases, and direct measurement in a controlled environment.\n",
    "\n",
    "5) Data Analysis: Analyze the collected data using statistical methods. Calculate the mean, median, mode, and standard deviation. Inferential statistics, like confidence intervals and hypothesis testing, can help generalize the findings to the entire population.\n",
    "\n",
    "6) Address Bias and Variability: Ensure the sample is representative of the population. Address potential biases (like selection bias or measurement bias) and take steps to minimize them. For instance, ensure that the sampling method does not disproportionately exclude certain groups.\n",
    "\n",
    "7) Report Findings: Present the findings with an emphasis on the limitations and the confidence level of the estimates.\n",
    "\n",
    "8) Continuous Monitoring and Updates: Human populations and their characteristics change over time. Regularly updating the data and methodology can help maintain the accuracy of the estimates.\n",
    "\n",
    "This approach, while not measuring every individual, provides a scientifically sound method to estimate the average height with a known degree of accuracy and confidence."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9329dd19",
   "metadata": {},
   "source": [
    "# Q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b93a626f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1.553856019598407,\n",
       " -0.816677814951721,\n",
       " -0.3396801531215125,\n",
       " 0.44086147532791964,\n",
       " 1.004585984763621,\n",
       " 1.2647665275800983]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = [4.5, 6.2, 7.3, 9.1, 10.4, 11]\n",
    "\n",
    "mean_X = np.mean(X)\n",
    "std_dev_X = np.std(X, ddof=0)\n",
    "\n",
    "z_scores = [(x - mean_X) / std_dev_X for x in X]\n",
    "z_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a812c3f",
   "metadata": {},
   "source": [
    "For 4.5: −1.55\n",
    "For 6.2: −0.82\n",
    "For 7.3: −0.34\n",
    "For 9.1: 0.44\n",
    "For 10.4: 1.00\n",
    "For 11: 1.26\n",
    "\n",
    "These z-scores indicate how many standard deviations each value is from the mean of the set. A negative z-score indicates a value below the mean, while a positive z-score indicates a value above the mean."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274ae73d",
   "metadata": {},
   "source": [
    "# Q8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f241ef9a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows of the data:\n",
      "   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
      "0   1   25           1      49     91107       4    1.6          1         0   \n",
      "1   2   45          19      34     90089       3    1.5          1         0   \n",
      "2   3   39          15      11     94720       1    1.0          1         0   \n",
      "3   4   35           9     100     94112       1    2.7          2         0   \n",
      "4   5   35           8      45     91330       4    1.0          2         0   \n",
      "\n",
      "   Personal Loan  Securities Account  CD Account  Online  CreditCard  \n",
      "0              0                   1           0       0           0  \n",
      "1              0                   1           0       0           0  \n",
      "2              0                   0           0       0           0  \n",
      "3              0                   0           0       0           0  \n",
      "4              0                   0           0       0           1  \n",
      "\n",
      "Summary statistics:\n",
      "                ID          Age   Experience       Income      ZIP Code  \\\n",
      "count  5000.000000  5000.000000  5000.000000  5000.000000   5000.000000   \n",
      "mean   2500.500000    45.338400    20.104600    73.774200  93152.503000   \n",
      "std    1443.520003    11.463166    11.467954    46.033729   2121.852197   \n",
      "min       1.000000    23.000000    -3.000000     8.000000   9307.000000   \n",
      "25%    1250.750000    35.000000    10.000000    39.000000  91911.000000   \n",
      "50%    2500.500000    45.000000    20.000000    64.000000  93437.000000   \n",
      "75%    3750.250000    55.000000    30.000000    98.000000  94608.000000   \n",
      "max    5000.000000    67.000000    43.000000   224.000000  96651.000000   \n",
      "\n",
      "            Family        CCAvg    Education     Mortgage  Personal Loan  \\\n",
      "count  5000.000000  5000.000000  5000.000000  5000.000000    5000.000000   \n",
      "mean      2.396400     1.937913     1.881000    56.498800       0.096000   \n",
      "std       1.147663     1.747666     0.839869   101.713802       0.294621   \n",
      "min       1.000000     0.000000     1.000000     0.000000       0.000000   \n",
      "25%       1.000000     0.700000     1.000000     0.000000       0.000000   \n",
      "50%       2.000000     1.500000     2.000000     0.000000       0.000000   \n",
      "75%       3.000000     2.500000     3.000000   101.000000       0.000000   \n",
      "max       4.000000    10.000000     3.000000   635.000000       1.000000   \n",
      "\n",
      "       Securities Account  CD Account       Online   CreditCard  \n",
      "count         5000.000000  5000.00000  5000.000000  5000.000000  \n",
      "mean             0.104400     0.06040     0.596800     0.294000  \n",
      "std              0.305809     0.23825     0.490589     0.455637  \n",
      "min              0.000000     0.00000     0.000000     0.000000  \n",
      "25%              0.000000     0.00000     0.000000     0.000000  \n",
      "50%              0.000000     0.00000     1.000000     0.000000  \n",
      "75%              0.000000     0.00000     1.000000     1.000000  \n",
      "max              1.000000     1.00000     1.000000     1.000000  \n",
      "\n",
      "Missing values in each column:\n",
      "ID                    0\n",
      "Age                   0\n",
      "Experience            0\n",
      "Income                0\n",
      "ZIP Code              0\n",
      "Family                0\n",
      "CCAvg                 0\n",
      "Education             0\n",
      "Mortgage              0\n",
      "Personal Loan         0\n",
      "Securities Account    0\n",
      "CD Account            0\n",
      "Online                0\n",
      "CreditCard            0\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\openpyxl\\worksheet\\_read_only.py:79: UserWarning: Unknown extension is not supported and will be removed\n",
      "  for idx, row in parser.parse():\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file_name = 'Bank Personal Loan Modelling.xlsx'\n",
    "data = pd.read_excel(file_name, sheet_name='Data')\n",
    "\n",
    "print(\"First few rows of the data:\")\n",
    "print(data.head())\n",
    "\n",
    "print(\"\\nSummary statistics:\")\n",
    "print(data.describe())\n",
    "\n",
    "print(\"\\nMissing values in each column:\")\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "52cc19c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                ID          Age   Experience       Income      ZIP Code  \\\n",
      "count  5000.000000  5000.000000  5000.000000  5000.000000   5000.000000   \n",
      "mean   2500.500000    45.338400    20.104600    73.774200  93152.503000   \n",
      "std    1443.520003    11.463166    11.467954    46.033729   2121.852197   \n",
      "min       1.000000    23.000000    -3.000000     8.000000   9307.000000   \n",
      "25%    1250.750000    35.000000    10.000000    39.000000  91911.000000   \n",
      "50%    2500.500000    45.000000    20.000000    64.000000  93437.000000   \n",
      "75%    3750.250000    55.000000    30.000000    98.000000  94608.000000   \n",
      "max    5000.000000    67.000000    43.000000   224.000000  96651.000000   \n",
      "\n",
      "            Family        CCAvg    Education     Mortgage  Personal Loan  \\\n",
      "count  5000.000000  5000.000000  5000.000000  5000.000000    5000.000000   \n",
      "mean      2.396400     1.937913     1.881000    56.498800       0.096000   \n",
      "std       1.147663     1.747666     0.839869   101.713802       0.294621   \n",
      "min       1.000000     0.000000     1.000000     0.000000       0.000000   \n",
      "25%       1.000000     0.700000     1.000000     0.000000       0.000000   \n",
      "50%       2.000000     1.500000     2.000000     0.000000       0.000000   \n",
      "75%       3.000000     2.500000     3.000000   101.000000       0.000000   \n",
      "max       4.000000    10.000000     3.000000   635.000000       1.000000   \n",
      "\n",
      "       Securities Account  CD Account       Online   CreditCard  \n",
      "count         5000.000000  5000.00000  5000.000000  5000.000000  \n",
      "mean             0.104400     0.06040     0.596800     0.294000  \n",
      "std              0.305809     0.23825     0.490589     0.455637  \n",
      "min              0.000000     0.00000     0.000000     0.000000  \n",
      "25%              0.000000     0.00000     0.000000     0.000000  \n",
      "50%              0.000000     0.00000     1.000000     0.000000  \n",
      "75%              0.000000     0.00000     1.000000     1.000000  \n",
      "max              1.000000     1.00000     1.000000     1.000000  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\openpyxl\\worksheet\\_read_only.py:79: UserWarning: Unknown extension is not supported and will be removed\n",
      "  for idx, row in parser.parse():\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file_name = 'Bank Personal Loan Modelling.xlsx'\n",
    "\n",
    "data = pd.read_excel(file_name, sheet_name='Data')\n",
    "\n",
    "summary_statistics = data.describe(include='all')\n",
    "\n",
    "print(summary_statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a4d7464",
   "metadata": {},
   "source": [
    "Here's the statistical summary for all the variables in the dataset:\n",
    "\n",
    "Count: Shows the number of non-missing values in each column.\n",
    "Mean: The average value for each column.\n",
    "Std (Standard Deviation): Measures the amount of variation or dispersion of the values.\n",
    "Min: The minimum value in each column.\n",
    "25% (First Quartile): The value below which 25% of the data falls.\n",
    "50% (Median): The middle value of the dataset.\n",
    "75% (Third Quartile): The value below which 75% of the data falls.\n",
    "Max: The maximum value in each column.\n",
    "For the Numerical Variables:\n",
    "\n",
    "ID: Ranges from 1 to 5000.\n",
    "Age: Ranges from 23 to 67 years, with a mean of approximately 45.34 years.\n",
    "Experience: Varies from -3 to 43 years, which may indicate some data entry errors as experience cannot be negative.\n",
    "Income: Annual income ranges from 8 to 224, with a mean of approximately 73.77.\n",
    "ZIP Code: Ranges from 9307 to 96651.\n",
    "Family: Family sizes range from 1 to 4, with an average size of approximately 2.40.\n",
    "CCAvg: Average monthly credit card spending ranges from 0 to 10, with a mean of approximately 1.94.\n",
    "Education: Levels vary from 1 to 3.\n",
    "Mortgage: Values range from 0 to 635.\n",
    "For the Categorical/Binary Variables:\n",
    "\n",
    "Personal Loan: Indicates whether the customer accepted a personal loan (1) or not (0).\n",
    "Securities Account: Indicates whether the customer has a securities account (1) or not (0).\n",
    "CD Account: Indicates whether the customer has a CD account (1) or not (0).\n",
    "Online: Indicates whether the customer uses online banking facilities (1) or not (0).\n",
    "CreditCard: Indicates whether the customer uses a credit card issued by the bank (1) or not (0)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3bb367",
   "metadata": {},
   "source": [
    "# Q9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "811e04db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\openpyxl\\worksheet\\_read_only.py:79: UserWarning: Unknown extension is not supported and will be removed\n",
      "  for idx, row in parser.parse():\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Income      73.774200\n",
       " CCAvg        1.937913\n",
       " Mortgage    56.498800\n",
       " dtype: float64,\n",
       " Income      64.0\n",
       " CCAvg        1.5\n",
       " Mortgage     0.0\n",
       " dtype: float64,\n",
       " Age           45.3384\n",
       " Experience    20.1046\n",
       " Family         2.3964\n",
       " Education      1.8810\n",
       " dtype: float64,\n",
       " Age           45.0\n",
       " Experience    20.0\n",
       " Family         2.0\n",
       " Education      2.0\n",
       " dtype: float64,\n",
       "    Age  Experience  Family  Education\n",
       " 0   35          32       1          1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file_name = 'Bank Personal Loan Modelling.xlsx'\n",
    "\n",
    "data_sheet = pd.read_excel(file_name, sheet_name='Data')\n",
    "\n",
    "continuous_vars = data_sheet[['Income', 'CCAvg', 'Mortgage']]\n",
    "discrete_vars = data_sheet[['Age', 'Experience', 'Family', 'Education']]\n",
    "\n",
    "\n",
    "\n",
    "mean_continuous = continuous_vars.mean()\n",
    "median_continuous = continuous_vars.median()\n",
    "\n",
    "mean_discrete = discrete_vars.mean()\n",
    "median_discrete = discrete_vars.median()\n",
    "mode_discrete = discrete_vars.mode()\n",
    "\n",
    "mean_continuous, median_continuous, mean_discrete, median_discrete, mode_discrete"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b22091",
   "metadata": {},
   "source": [
    "Here are the measures of central tendency for the selected continuous and discrete variables in the dataset:\n",
    "\n",
    "Continuous Variables:\n",
    "\n",
    "Income:\n",
    "Mean: 73.7742\n",
    "Median: 64.0\n",
    "CCAvg (Average Credit Card Spending):\n",
    "Mean: 1.9379\n",
    "Median: 1.5\n",
    "Mortgage:\n",
    "Mean: 56.4988\n",
    "Median: 0.0\n",
    "Discrete Variables:\n",
    "\n",
    "Age:\n",
    "Mean: 45.3384 years\n",
    "Median: 45.0 years\n",
    "Mode: 35 years\n",
    "Experience:\n",
    "Mean: 20.1046 years\n",
    "Median: 20.0 years\n",
    "Mode: 32 years\n",
    "Family:\n",
    "Mean: 2.3964 members\n",
    "Median: 2.0 members\n",
    "Mode: 1 member\n",
    "Education:\n",
    "Mean: 1.881 (1: Undergrad; 2: Graduate; 3: Advanced/Professional)\n",
    "Median: 2.0\n",
    "Mode: 1 (Undergrad)\n",
    "These measures provide insights into the central tendency of each variable. For instance, the median income is lower than the mean income, which might suggest a right-skewed distribution. The median mortgage value is zero, indicating that more than half of the customers do not have a mortgage. The mode for education being 1 suggests that the majority of customers are undergraduates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb38ac81",
   "metadata": {},
   "source": [
    "# Q10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e1a807",
   "metadata": {},
   "source": [
    "Performing Correlation Analysis: This method measures the strength and direction of the linear relationship between two variables. The Pearson correlation coefficient is commonly used for this purpose. However, it's important to note that correlation does not imply causation.\n",
    "\n",
    "Scatter Plot: A visual examination using a scatter plot can provide insights into the relationship between age and experience. If the points roughly form a line, it suggests a linear relationship.\n",
    "\n",
    "    Correlation Coefficient:\n",
    "\n",
    "If the correlation coefficient is close to 1, it indicates a strong positive linear relationship between age and experience (as age increases, experience also tends to increase).\n",
    "If the coefficient is close to -1, it indicates a strong negative linear relationship (as age increases, experience decreases, which would be unusual in this context).\n",
    "A coefficient near 0 suggests a weak or no linear relationship between age and experience.\n",
    "\n",
    "    Scatter Plot Analysis:\n",
    "\n",
    "Look for a pattern or trend in the scatter plot. A linear trend (points roughly forming a line) would corroborate a strong correlation.\n",
    "If the points are widely scattered without any clear pattern, this suggests a weak relationship.\n",
    "Also, look for any outliers or unusual patterns that might indicate errors in data or special cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "748dec5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\openpyxl\\worksheet\\_read_only.py:79: UserWarning: Unknown extension is not supported and will be removed\n",
      "  for idx, row in parser.parse():\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Age and Experience:\n",
      "                 Age  Experience\n",
      "Age         1.000000    0.994215\n",
      "Experience  0.994215    1.000000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "file_name = 'Bank Personal Loan Modelling.xlsx'\n",
    "data_sheet = pd.read_excel(file_name, sheet_name='Data')\n",
    "\n",
    "# Correlation Analysis\n",
    "correlation = data_sheet[['Age', 'Experience']].corr()\n",
    "\n",
    "# Scatter Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='Age', y='Experience', data=data_sheet)\n",
    "plt.title('Scatter Plot of Age vs Experience')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Experience')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print(\"Correlation between Age and Experience:\")\n",
    "print(correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73891a9b",
   "metadata": {},
   "source": [
    "# Q11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4bb087b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\openpyxl\\worksheet\\_read_only.py:79: UserWarning: Unknown extension is not supported and will be removed\n",
      "  for idx, row in parser.parse():\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 1472)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = 'Bank Personal Loan Modelling.xlsx'\n",
    "data_sheet = pd.read_excel(file_name, sheet_name='Data')\n",
    "\n",
    "most_frequent_family_size = data_sheet['Family'].mode()[0]\n",
    "most_frequent_family_size_count = data_sheet['Family'].value_counts()[most_frequent_family_size]\n",
    "\n",
    "most_frequent_family_size, most_frequent_family_size_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c7c49c",
   "metadata": {},
   "source": [
    "The most frequent family size present in this dataset is a family of 1 member. This family size occurs 1,472 times in the dataset, making it the most common."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9abc2ea",
   "metadata": {},
   "source": [
    "# Q12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bd8c61bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.39814097758612"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the coefficient of variation for the 'Income' variable\n",
    "income_mean = data_sheet['Income'].mean()\n",
    "income_std = data_sheet['Income'].std()\n",
    "\n",
    "# Coefficient of Variation = (Standard Deviation / Mean) * 100\n",
    "income_coefficient_of_variation = (income_std / income_mean) * 100\n",
    "income_coefficient_of_variation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07688ac",
   "metadata": {},
   "source": [
    "The coefficient of variation for the 'Income' variable in the dataset is approximately 62.40%. This indicates a moderate to high level of variability in income relative to the mean income."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd047ff",
   "metadata": {},
   "source": [
    "# Q13"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec54a4c",
   "metadata": {},
   "source": [
    "Replacing zeros with the median of the non-zero mortgage values. This approach is often preferred over the mean in cases of skewed distributions, like with mortgage values. Let's proceed with this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76197519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(153.0,\n",
       "    ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
       " 0   1   25           1      49     91107       4    1.6          1       153   \n",
       " 1   2   45          19      34     90089       3    1.5          1       153   \n",
       " 2   3   39          15      11     94720       1    1.0          1       153   \n",
       " 3   4   35           9     100     94112       1    2.7          2       153   \n",
       " 4   5   35           8      45     91330       4    1.0          2       153   \n",
       " \n",
       "    Personal Loan  Securities Account  CD Account  Online  CreditCard  \n",
       " 0              0                   1           0       0           0  \n",
       " 1              0                   1           0       0           0  \n",
       " 2              0                   0           0       0           0  \n",
       " 3              0                   0           0       0           0  \n",
       " 4              0                   0           0       0           1  )"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the median of non-zero mortgage values\n",
    "non_zero_mortgage_median = data_sheet[data_sheet['Mortgage'] > 0]['Mortgage'].median()\n",
    "\n",
    "# Imputing zeros in the 'Mortgage' column with the median of non-zero values\n",
    "imputed_data = data_sheet.copy()\n",
    "imputed_data['Mortgage'] = imputed_data['Mortgage'].replace(0, non_zero_mortgage_median)\n",
    "\n",
    "non_zero_mortgage_median, imputed_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42432b8",
   "metadata": {},
   "source": [
    "The median value of the non-zero mortgage amounts, which is $153,000. This approach assumes that the zeros represent missing or unrecorded data, and the median provides a reasonable estimate that is not skewed by extremely high or low mortgage values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863c45a9",
   "metadata": {},
   "source": [
    "# Q14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1746890d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Average Experience with Credit Card': 20.263945578231294,\n",
       " 'Average Experience without Credit Card': 20.03824362606232,\n",
       " 'Experience T-test': Ttest_indResult(statistic=0.6339929423550292, pvalue=0.5261144417207964),\n",
       " 'Average Income with Credit Card': 73.60408163265306,\n",
       " 'Average Income without Credit Card': 73.84504249291784,\n",
       " 'Income T-test': Ttest_indResult(statistic=-0.16861226248095137, pvalue=0.8661084574127353)}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "credit_card_group = data_sheet[data_sheet['CreditCard'] == 1]\n",
    "no_credit_card_group = data_sheet[data_sheet['CreditCard'] == 0]\n",
    "\n",
    "avg_exp_credit_card = credit_card_group['Experience'].mean()\n",
    "avg_inc_credit_card = credit_card_group['Income'].mean()\n",
    "\n",
    "avg_exp_no_credit_card = no_credit_card_group['Experience'].mean()\n",
    "avg_inc_no_credit_card = no_credit_card_group['Income'].mean()\n",
    "\n",
    "t_test_exp = ttest_ind(credit_card_group['Experience'], no_credit_card_group['Experience'])\n",
    "t_test_inc = ttest_ind(credit_card_group['Income'], no_credit_card_group['Income'])\n",
    "\n",
    "{\n",
    "    \"Average Experience with Credit Card\": avg_exp_credit_card,\n",
    "    \"Average Experience without Credit Card\": avg_exp_no_credit_card,\n",
    "    \"Experience T-test\": t_test_exp,\n",
    "    \"Average Income with Credit Card\": avg_inc_credit_card,\n",
    "    \"Average Income without Credit Card\": avg_inc_no_credit_card,\n",
    "    \"Income T-test\": t_test_inc\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d0ec96f",
   "metadata": {},
   "source": [
    "The steps are as follows:\n",
    "\n",
    "    Group the Data: Separate the customers into two groups based on credit card ownership.\n",
    "    Calculate Averages: Compute the average experience and income for each group.\n",
    "    Statistical Test: Perform an independent samples t-test to see if the differences in means are statistically significant.\n",
    "\n",
    "\n",
    "    The analysis comparing customers with and without credit cards yields the following results:\n",
    "\n",
    "    Experience\n",
    "    Average Experience with Credit Card: 20.26 years\n",
    "    Average Experience without Credit Card: 20.04 years\n",
    "    Experience T-test:\n",
    "    Statistic: 0.63\n",
    "    p-value: 0.526\n",
    "\n",
    "    Income\n",
    "    Average Income with Credit Card: 73,604\n",
    "    Average Income without Credit Card: 73,845\n",
    "    Income T-test:\n",
    "    Statistic: -0.17\n",
    "    p-value: 0.866\n",
    "\n",
    "    Interpretation\n",
    "Experience: The average experience of customers is slightly higher in the group with credit cards, but the difference is not statistically significant (p-value > 0.05).\n",
    "\n",
    "Income: Similarly, the average income is slightly higher in the group without credit cards, but this difference is also not statistically significant (p-value > 0.05).\n",
    "\n",
    "In conclusion, owning a credit card does not appear to have a significant impact on the experience or income of the customers in this dataset. The differences observed are minimal and not statistically significant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3368a423",
   "metadata": {},
   "source": [
    "# Q15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2acac8cb",
   "metadata": {},
   "source": [
    "Outliers in a dataset can be identified using various methods. One of the most common methods is to visualize the data using box plots, which are excellent for detecting outliers in numerical variables. Outliers typically appear as points outside the whiskers of the box plot.\n",
    "\n",
    "For this dataset, considering its features, we might want to check for outliers in variables like 'Income', 'CCAvg' (average spending on credit cards per month), and 'Mortgage', as these financial metrics are more likely to have outliers due to their nature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3923ef17",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "sns.boxplot(ax=axes[0], x=data_sheet['Income']).set_title('Income')\n",
    "sns.boxplot(ax=axes[1], x=data_sheet['CCAvg']).set_title('CCAvg (Credit Card Average Spending)')\n",
    "sns.boxplot(ax=axes[2], x=data_sheet['Mortgage']).set_title('Mortgage')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0fd4766",
   "metadata": {},
   "source": [
    "The box plots for 'Income', 'CCAvg' (Credit Card Average Spending), and 'Mortgage' reveal the presence of outliers in these variables:\n",
    "\n",
    "Income: There are several points above the upper whisker, indicating outliers with significantly higher income than the majority.\n",
    "\n",
    "CCAvg (Credit Card Average Spending): Similar to 'Income', there are numerous points beyond the upper whisker, showing customers with unusually high credit card spending.\n",
    "\n",
    "Mortgage: This variable also has many points above the upper whisker, indicating customers with unusually high mortgage values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3eee7f",
   "metadata": {},
   "source": [
    "# Q16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c19b6fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1     22.0\n",
       "0.2     33.0\n",
       "0.3     42.0\n",
       "0.4     52.0\n",
       "0.5     64.0\n",
       "0.6     78.0\n",
       "0.7     88.3\n",
       "0.8    113.0\n",
       "0.9    145.0\n",
       "Name: Income, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the deciles for the 'Income' variable\n",
    "income_deciles = data_sheet['Income'].quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
    "income_deciles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fec6dde",
   "metadata": {},
   "source": [
    "# Q17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c201b86f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            20.0\n",
       "Experience     20.0\n",
       "Income         59.0\n",
       "CCAvg           1.8\n",
       "Mortgage      101.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the Interquartile Range (IQR) for all the quantitative and continuous variables\n",
    "# Identifying continuous variables: Age, Experience, Income, CCAvg, and Mortgage\n",
    "\n",
    "quantitative_continuous_vars = ['Age', 'Experience', 'Income', 'CCAvg', 'Mortgage']\n",
    "iqr_values = data_sheet[quantitative_continuous_vars].apply(lambda x: x.quantile(0.75) - x.quantile(0.25))\n",
    "\n",
    "iqr_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185524f5",
   "metadata": {},
   "source": [
    "The Interquartile Range (IQR) for the quantitative and continuous variables in the dataset is as follows:\n",
    "\n",
    "Age: IQR = 20 years\n",
    "Experience: IQR = 20 years\n",
    "Income: IQR = 59,000\n",
    "CCAvg (Average Credit Card Spending per Month): IQR = 1.8K\n",
    "Mortgage: IQR = 101,000\n",
    "The IQR is a measure of statistical dispersion and is the difference between the 75th and 25th percentiles. It is used to describe the middle 50 percent of values when ordered by ascending value, providing an understanding of the spread of the data and potentially highlighting the presence of outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30528ccc",
   "metadata": {},
   "source": [
    "# Q18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a9160b",
   "metadata": {},
   "source": [
    "To determine whether higher-income holders spend more on credit cards, we can analyze the relationship between 'Income' and 'CCAvg' (Average Credit Card Spending per Month). This can be done using correlation analysis and visual representation.\n",
    "\n",
    "Correlation Analysis: We will calculate the Pearson correlation coefficient between 'Income' and 'CCAvg'. This coefficient ranges from -1 to 1, where 1 indicates a strong positive relationship, -1 indicates a strong negative relationship, and 0 indicates no relationship.\n",
    "\n",
    "Visual Representation: A scatter plot can be used to visually inspect the relationship between these two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "06e1d992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.645992591917365"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the Pearson correlation coefficient between 'Income' and 'CCAvg'\n",
    "correlation_income_ccavg = data_sheet[['Income', 'CCAvg']].corr().iloc[0, 1]\n",
    "\n",
    "# Creating a scatter plot to visually inspect the relationship\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='Income', y='CCAvg', data=data_sheet)\n",
    "plt.title('Relationship between Income and Credit Card Spending')\n",
    "plt.xlabel('Income ($000)')\n",
    "plt.ylabel('Average Credit Card Spending per Month ($000)')\n",
    "plt.show()\n",
    "\n",
    "correlation_income_ccavg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b1ea8bc",
   "metadata": {},
   "source": [
    "The Pearson correlation coefficient between 'Income' and 'CCAvg' (Average Credit Card Spending per Month) is approximately 0.646. This value indicates a moderately strong positive relationship between income and credit card spending. In other words, as income increases, credit card spending tends to increase as well.\n",
    "\n",
    "The scatter plot visually supports this finding, showing a general trend where higher income is associated with higher average credit card spending. While there is variability in the data, the overall pattern suggests that higher-income holders do indeed tend to spend more on their credit cards."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e7d7bd",
   "metadata": {},
   "source": [
    "# Q19"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45bae487",
   "metadata": {},
   "source": [
    "To determine if customers using bank internet facilities have higher incomes, we can compare the average incomes between two groups: those who use internet banking facilities and those who do not. This involves:\n",
    "\n",
    "Grouping the Data: Split the customers into two groups based on their usage of internet banking services.\n",
    "\n",
    "Calculating Average Incomes: Compute the average income for each group.\n",
    "\n",
    "Statistical Test: Conduct an independent samples t-test to determine if the difference in average incomes is statistically significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "58e8e58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Average Income with Online Banking': 74.31166219839142,\n",
       " 'Average Income without Online Banking': 72.97867063492063,\n",
       " 'Income T-test': Ttest_indResult(statistic=1.0044106203656553, pvalue=0.315229357162428)}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the data based on internet banking usage\n",
    "online_banking_group = data_sheet[data_sheet['Online'] == 1]\n",
    "no_online_banking_group = data_sheet[data_sheet['Online'] == 0]\n",
    "\n",
    "# Calculating average incomes for each group\n",
    "avg_income_online = online_banking_group['Income'].mean()\n",
    "avg_income_no_online = no_online_banking_group['Income'].mean()\n",
    "\n",
    "# Performing independent samples t-test for Income\n",
    "t_test_income_online = ttest_ind(online_banking_group['Income'], no_online_banking_group['Income'])\n",
    "\n",
    "{\n",
    "    \"Average Income with Online Banking\": avg_income_online,\n",
    "    \"Average Income without Online Banking\": avg_income_no_online,\n",
    "    \"Income T-test\": t_test_income_online\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a02e0ef",
   "metadata": {},
   "source": [
    "    Average Income\n",
    "Average Income with Online Banking: Approximately 74,312\n",
    "Average Income without Online Banking: Approximately 72,979\n",
    "\n",
    "    Statistical Test (Independent Samples T-test)\n",
    "Statistic: 1.004\n",
    "p-value: 0.315\n",
    "\n",
    "    Interpretation\n",
    "The average income is slightly higher for customers who use online banking facilities compared to those who do not.\n",
    "However, the statistical test (t-test) indicates that this difference is not statistically significant (p-value > 0.05).\n",
    "In conclusion, while customers using online banking facilities have a marginally higher average income, the difference is not significant enough to assert that internet banking usage is associated with higher income in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb619c4",
   "metadata": {},
   "source": [
    "# Q20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "68c3330a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.538175\n",
       "1   -0.864023\n",
       "2   -1.363657\n",
       "3    0.569708\n",
       "4   -0.625068\n",
       "Name: Income, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_mean = data_sheet['Income'].mean()\n",
    "income_std = data_sheet['Income'].std()\n",
    "\n",
    "# Z score = (X - mean) / std\n",
    "z_scores_income = (data_sheet['Income'] - income_mean) / income_std\n",
    "\n",
    "z_scores_income.head() "
   ]
  }
 ],
 "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",
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 },
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}