replaced Height and Weight

Author: Rayycoding

.idea/.gitignore

@@ -3,9 +3,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Introduction to Probability and Statistics\r\n",
-    "## Assignment\r\n",
-    "\r\n",
+    "## Introduction to Probability and Statistics\n",
+    "## Assignment\n",
+    "\n",
     "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
    ],
    "metadata": {}
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "\r\n",
-    "In this dataset, columns as the following:\r\n",
-    "* Age and sex are self-explanatory\r\n",
-    "* BMI is body mass index\r\n",
-    "* BP is average blood pressure\r\n",
-    "* S1 through S6 are different blood measurements\r\n",
-    "* Y is the qualitative measure of disease progression over one year\r\n",
-    "\r\n",
-    "Let's study this dataset using methods of probability and statistics.\r\n",
-    "\r\n",
+    "\n",
+    "In this dataset, columns as the following:\n",
+    "* Age and sex are self-explanatory\n",
+    "* BMI is body mass index\n",
+    "* BP is average blood pressure\n",
+    "* S1 through S6 are different blood measurements\n",
+    "* Y is the qualitative measure of disease progression over one year\n",
+    "\n",
+    "Let's study this dataset using methods of probability and statistics.\n",
+    "\n",
     "### Task 1: Compute mean values and variance for all values"
    ],
    "metadata": {}
@@ -201,8 +201,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
-    "\r\n",
+    "### Task 4: Test the correlation between different variables and disease progression (Y)\n",
+    "\n",
     "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
    ],
    "metadata": {}
@@ -249,4 +249,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

.idea/Data-Science-For-Beginners.iml

@@ -3,9 +3,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Introduction to Probability and Statistics\r\n",
-    "## Assignment\r\n",
-    "\r\n",
+    "## Introduction to Probability and Statistics\n",
+    "## Assignment\n",
+    "\n",
     "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
    ],
    "metadata": {}
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "\r\n",
-    "In this dataset, columns as the following:\r\n",
-    "* Age and sex are self-explanatory\r\n",
-    "* BMI is body mass index\r\n",
-    "* BP is average blood pressure\r\n",
-    "* S1 through S6 are different blood measurements\r\n",
-    "* Y is the qualitative measure of disease progression over one year\r\n",
-    "\r\n",
-    "Let's study this dataset using methods of probability and statistics.\r\n",
-    "\r\n",
+    "\n",
+    "In this dataset, columns as the following:\n",
+    "* Age and sex are self-explanatory\n",
+    "* BMI is body mass index\n",
+    "* BP is average blood pressure\n",
+    "* S1 through S6 are different blood measurements\n",
+    "* Y is the qualitative measure of disease progression over one year\n",
+    "\n",
+    "Let's study this dataset using methods of probability and statistics.\n",
+    "\n",
     "### Task 1: Compute mean values and variance for all values"
    ],
    "metadata": {}
@@ -355,7 +355,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -447,7 +447,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -486,8 +486,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -538,8 +538,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -593,18 +593,18 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Conclusions:\r\n",
-    "* Age - normal\r\n",
-    "* Sex - uniform\r\n",
+    "Conclusions:\n",
+    "* Age - normal\n",
+    "* Sex - uniform\n",
     "* BMI, Y - hard to tell"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
-    "\r\n",
+    "### Task 4: Test the correlation between different variables and disease progression (Y)\n",
+    "\n",
     "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
    ],
    "metadata": {}
@@ -847,7 +847,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Conclusion:\r\n",
+    "Conclusion:\n",
     "* The strongest correlation of Y is BMI and S5 (blood sugar). This sounds reasonable."
    ],
    "metadata": {}
@@ -856,10 +856,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -888,9 +888,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -942,4 +942,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}