diff --git a/notebook/1 . EDA STUDENT PERFORMANCE .ipynb b/notebook/1 . EDA STUDENT PERFORMANCE .ipynb
index d2df5aa2..e037b25c 100644
--- a/notebook/1 . EDA STUDENT PERFORMANCE .ipynb	
+++ b/notebook/1 . EDA STUDENT PERFORMANCE .ipynb	
@@ -1412,6 +1412,24 @@
     "#### BIVARIATE ANALYSIS ( Is gender has any impact on student's performance ? ) "
    ]
   },
+{
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "526d49f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n"
+
+"# Create an instance of LabelEncoder\n"
+"label_encoder = LabelEncoder()\n"
+
+"# Fit and transform the 'gender' column\n"
+"df['gender_encoded'] = label_encoder.fit_transform(df['gender'])\n"
+"df['gender_encoded']\n"
+"non_numeric_data = pd.to_numeric(df['gender_encoded'],errors='coerce').isna().any() # to check is there any numeric data non_numeric_data\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1419,7 +1437,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "gender_group = df.groupby('gender').mean()\n",
+    "gender_group = df.groupby('gender_encoded').mean()\n",
     "gender_group"
    ]
   },
@@ -1430,24 +1448,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "\n",
-    "X = ['Total Average','Math Average']\n",
-    "\n",
-    "\n",
-    "female_scores = [gender_group['average'][0], gender_group['math score'][0]]\n",
-    "male_scores = [gender_group['average'][1], gender_group['math score'][1]]\n",
-    "\n",
-    "X_axis = np.arange(len(X))\n",
-    "  \n",
-    "plt.bar(X_axis - 0.2, male_scores, 0.4, label = 'Male')\n",
-    "plt.bar(X_axis + 0.2, female_scores, 0.4, label = 'Female')\n",
-    "  \n",
-    "plt.xticks(X_axis, X)\n",
-    "plt.ylabel(\"Marks\")\n",
-    "plt.title(\"Total average v/s Math average marks of both the genders\", fontweight='bold')\n",
-    "plt.legend()\n",
-    "plt.show()"
+   "import matplotlib.pyplot as plt\n",
+"import numpy as np\n",
+"plt.figure(figsize=(10, 8))\n",
+"X = ['Total Average', 'Math Average']\n",
+"female_scores = [df.loc[0, 'average'], df.loc[0, 'math_score']]\n",
+"male_scores = [df.loc[1, 'average'], df.loc[1, 'math_score']]\n",
+"X_axis = np.arange(len(X)) \n",
+"plt.bar(X_axis - 0.2, male_scores, 0.4, label='Male') \n",
+"plt.bar(X_axis + 0.2, female_scores, 0.4, label='Female') \n",
+"plt.xticks(X_axis, X) \n",
+"plt.ylabel("Marks") \n",
+"plt.title("Total average v/s Math average marks of both genders",fontweight='bold') \n",
+"plt.legend() \n",
+"plt.show()"
    ]
   },
   {