featureEngineering6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "030cc00a-dba1-4b9d-a868-4197706f61db",
   "metadata": {},
   "source": [
    "### Pearson correlation coefficient is a measure of the linear relationship between two variables. Suppose you have collected data on the amount of time students spend studying for an exam and their final exam scores. Calculate the Pearson correlation coefficient between these two variables and interpret the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb8f454a-d8ac-41e9-98cf-422efe04c036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Coefficient: 0.9639326334296814\n",
      "Interpretation: There is a positive linear relationship.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "study_hours_per_day = [2, 3, 1, 4, 2, 5, 3, 6, 4, 5]\n",
    "exam_scores =  [70, 78, 67, 90, 78, 95, 85, 98, 87, 94]\n",
    "\n",
    "corr_coefficient, _ = pearsonr(study_hours_per_day, exam_scores)\n",
    "\n",
    "if corr_coefficient > 0:\n",
    "    interpretation = \"There is a positive linear relationship.\"\n",
    "elif corr_coefficient < 0:\n",
    "    interpretation = \"There is a negative linear relationship.\"\n",
    "else:\n",
    "    interpretation = \"There is little to no linear relationship.\"\n",
    "\n",
    "print(f\"Pearson Correlation Coefficient: {corr_coefficient}\")\n",
    "print(f\"Interpretation: {interpretation}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0e7875-ed4a-44bd-b059-659932d25f8b",
   "metadata": {},
   "source": [
    "The Pearson correlation coefficient (r) will range from -1 to 1.\n",
    "If r is close to 1, it indicates a strong positive linear relationship. This means that as students spend more time studying, their exam scores tend to be higher.\n",
    "If r is close to -1, it indicates a strong negative linear relationship. This means that as students spend more time studying, their exam scores tend to be lower.\n",
    "If r is close to 0, it suggests a weak or no linear relationship. In this case, there is little to no linear association between study time and exam scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecfeb0a-a242-4a63-ad8e-26cd39e9febc",
   "metadata": {},
   "source": [
    "### Spearman's rank correlation is a measure of the monotonic relationship between two variables. Suppose you have collected data on the amount of sleep individuals get each night and their overall job satisfaction level on a scale of 1 to 10. Calculate the Spearman's rank correlation between these two variables and interpret the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c1efa790-5fd3-41c4-b64f-a1859422cc3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman's Rank Correlation Coefficient (ρ): 0.92\n",
      "Interpretation: There is a positive monotonic relationship.\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "sleep_hours =      [7, 6, 8, 5, 7, 6, 8]\n",
    "job_satisfaction = [7, 7, 8, 4, 8, 6, 9]\n",
    "\n",
    "rho, _ = stats.spearmanr(sleep_hours, job_satisfaction)\n",
    "\n",
    "if rho > 0:\n",
    "    interpretation = \"There is a positive monotonic relationship.\"\n",
    "elif rho < 0:\n",
    "    interpretation = \"There is a negative monotonic relationship.\"\n",
    "else:\n",
    "    interpretation = \"There is little to no monotonic relationship.\"\n",
    "\n",
    "print(f\"Spearman's Rank Correlation Coefficient (ρ): {rho:.2f}\")\n",
    "print(f\"Interpretation: {interpretation}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c2244e-8abb-4bbf-9d17-d16f60bf840b",
   "metadata": {},
   "source": [
    "If ρ is close to 1, it indicates a strong positive monotonic relationship. This means that as the amount of sleep increases, job satisfaction tends to increase.\n",
    "If ρ is close to -1, it indicates a strong negative monotonic relationship. This means that as the amount of sleep increases, job satisfaction tends to decrease.\n",
    "If ρ is close to 0, it suggests a weak or no monotonic relationship. In this case, there is little to no consistent monotonic association between sleep and job satisfaction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64662fd5-30e3-41f8-8f5d-1914c79e5b33",
   "metadata": {},
   "source": [
    "###  Suppose you are conducting a study to examine the relationship between the number of hours of exercise per week and body mass index (BMI) in a sample of adults. You collected data on both variables for 50 participants. Calculate the Pearson correlation coefficient and the Spearman's rank correlation between these two variables and compare the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2f26898-4291-4775-8e30-a91d2820d17a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Coefficient: 0.72\n",
      "Spearman's Rank Correlation Coefficient: 0.75\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "\n",
    "exercise_hours = [3, 5, 2, 4, 6, 3, 7, 5, 4, 6, 2, 3, 4, 5, 2, 6, 7, 3, 5, 4,\n",
    "                  6, 2, 3, 4, 5, 2, 6, 7, 3, 5, 4, 6, 2, 3, 4, 5, 2, 6, 7, 3,\n",
    "                  5, 4, 6, 2, 3, 4, 5, 2, 5, 7]\n",
    "bmi = [25.2, 29.1, 23.4, 27.8, 31.2, 26.0, 32.5, 28.7, 27.3, 30.9, 24.1, 25.7,\n",
    "       28.5, 29.8, 23.9, 31.5, 32.8, 26.2, 29.3, 27.6, 31.0, 24.8, 26.5, 28.3,\n",
    "       30.2, 23.2, 31.8, 33.1, 26.6, 29.5, 27.9, 31.1, 24.5, 26.9, 28.1, 30.1,\n",
    "       23.5, 32.0, 33.2, 27.1, 29.7, 27.4, 30.4, 23.8, 31.9, 33.0, 27.0, 29.9, 27.5, 22]\n",
    "\n",
    "pearson_corr, _ = pearsonr(exercise_hours, bmi)\n",
    "\n",
    "spearman_corr, _ = spearmanr(exercise_hours, bmi)\n",
    "\n",
    "print(f\"Pearson Correlation Coefficient: {pearson_corr:.2f}\")\n",
    "print(f\"Spearman's Rank Correlation Coefficient: {spearman_corr:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6ff9f5-08a9-458d-930f-6ac7fe3eb7a0",
   "metadata": {},
   "source": [
    "1. **Pearson Correlation Coefficient (r = 0.72):**\n",
    "   - The Pearson correlation coefficient measures the linear relationship between two variables.\n",
    "   - A value of 0.72 suggests a moderately strong positive linear relationship between the number of hours of exercise per week and body mass index (BMI).\n",
    "   - This means that as the number of hours of exercise per week increases, BMI tends to increase, but the relationship may not be perfect or strictly linear.\n",
    "\n",
    "2. **Spearman's Rank Correlation Coefficient (ρ = 0.75):**\n",
    "   - Spearman's rank correlation coefficient measures the monotonic relationship between two variables, which captures non-linear relationships as well.\n",
    "   - A value of 0.75 indicates a moderately strong positive monotonic relationship between the number of hours of exercise per week and BMI.\n",
    "   - This suggests that there is a consistent trend: as the number of hours of exercise per week increases, BMI tends to increase, regardless of whether the relationship is strictly linear."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c5b034-9a7d-4823-84e6-3310192e7543",
   "metadata": {},
   "source": [
    "### A researcher is interested in examining the relationship between the number of hours individuals spend watching television per day and their level of physical activity. The researcher collected data on both variables from a sample of 50 participants. Calculate the Pearson correlation coefficient between these two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "94364c6e-88d0-48c2-88df-74bb0b509565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Coefficient: -0.32\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "tv_hours = [2.5, 3.0, 4.5, 2.0, 5.0, 3.5, 2.0, 4.0, 6.0, 1.5, 2.5, 3.0, 4.0, 5.5, 2.0, 3.5, 2.0, 4.0, 6.0, 1.5,\n",
    "            3.0, 4.5, 2.0, 5.0, 3.5, 2.0, 4.0, 6.0, 1.5, 2.5, 3.0, 4.0, 5.5, 2.0, 3.5, 2.0, 4.0, 6.0, 1.5, 2.5,\n",
    "            3.0, 4.0, 5.5, 2.0, 3.5, 2.0, 4.0, 6.0, 5.0, 5.5]\n",
    "\n",
    "physical_activity = [3, 2, 1, 4, 2, 3, 4, 2, 1, 5, 3, 2, 1, 4, 2, 3, 4, 2, 1, 5,\n",
    "                     2, 3, 4, 2, 1, 5, 3, 2, 1, 4, 2, 3, 4, 2, 1, 5, 3, 2, 1, 4,\n",
    "                     2, 3, 4, 2, 1, 5, 3, 2, 1, 4]\n",
    "\n",
    "pearson_corr = np.corrcoef(tv_hours, physical_activity)[0, 1]\n",
    "\n",
    "print(f\"Pearson Correlation Coefficient: {pearson_corr:.2f}\")"
   ]
  },
  {
   "attachments": {
    "66de70bd-81f8-44d8-879c-8338de6d1258.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEoCAYAAAAE6cViAAAAAXNSR0IArs4c6QAAAARnQU1BAACx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\nF8fTl+j8AAAAAElFTkSuQmCC\n"
    }
   },
   "cell_type": "markdown",
   "id": "6f2e42fa-29cd-422f-acfe-9bdf610bb094",
   "metadata": {},
   "source": [
    "###  A survey was conducted to examine the relationship between age and preference for a particular brand of soft drink. The survey results are shown below:\n",
    "\n",
    "![Given data.png](attachment:66de70bd-81f8-44d8-879c-8338de6d1258.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "caa90dfb-435e-42a1-b0ef-79d13ecf0216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F_onewayResult(statistic=72.98774740810559, pvalue=6.592754475599983e-06)\n",
      "There is a statistically significant relationship between age and preference label.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "\n",
    "data = {'Age': [25, 42, 37, 19, 31, 27],\n",
    "        'Soft_Drink_Preference': ['Coke', 'Pepsi', 'Mountain Dew', 'Coke', 'Pepsi', 'Coke']}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "df['Preference_Label'] = label_encoder.fit_transform(df['Soft_Drink_Preference'])\n",
    "\n",
    "result = stats.f_oneway(df['Age'], df['Preference_Label'])\n",
    "\n",
    "print(result)\n",
    "if result.pvalue < 0.05:\n",
    "    print(\"There is a statistically significant relationship between age and preference label.\")\n",
    "else:\n",
    "    print(\"There is no statistically significant relationship between age and preference label.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c5b1b529-bdf0-442d-9dcd-b07af8381da5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  count       mean       std   min    25%   50%    75%   max\n",
      "Preference_Label                                                            \n",
      "0                   3.0  23.666667  4.163332  19.0  22.00  25.0  26.00  27.0\n",
      "1                   1.0  37.000000       NaN  37.0  37.00  37.0  37.00  37.0\n",
      "2                   2.0  36.500000  7.778175  31.0  33.75  36.5  39.25  42.0\n"
     ]
    }
   ],
   "source": [
    "summary_stats = df.groupby('Preference_Label')['Age'].describe()\n",
    "print(summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4b2e10ef-8956-45ee-9f89-3481eb0e9559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.boxplot(data=df, x='Preference_Label', y='Age', hue='Soft_Drink_Preference', palette='deep')\n",
    "plt.xlabel('Preference_Label')\n",
    "plt.ylabel('Age')\n",
    "plt.title('Relationship Between Age and Preference Label (Box Plot)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6702b51-de6f-4115-ae05-e509b001b62b",
   "metadata": {},
   "source": [
    "###  A company is interested in examining the relationship between the number of sales calls made per day and the number of sales made per week. The company collected data on both variables from a sample of 30 sales representatives. Calculate the Pearson correlation coefficient between these two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7c674822-2912-45d9-82b4-72600f65758f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Coefficient: 0.99\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "sales_calls_per_day = [20, 18, 22, 17, 19, 23, 21, 16, 18, 20,\n",
    "                       19, 17, 22, 18, 20, 19, 21, 23, 17, 16,\n",
    "                       24, 22, 19, 20, 18, 23, 17, 21, 19, 20]\n",
    "\n",
    "sales_per_week = [150, 140, 160, 130, 145, 165, 155, 125, 135, 150,\n",
    "                  140, 130, 160, 140, 150, 145, 155, 165, 130, 125,\n",
    "                  170, 160, 145, 150, 140, 165, 130, 155, 140, 150]\n",
    "\n",
    "correlation, _ = pearsonr(sales_calls_per_day, sales_per_week)\n",
    "\n",
    "print(f\"Pearson Correlation Coefficient: {correlation:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c98711d-ce03-43f2-887c-ec2177a3dbe9",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}