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Update 1 the gender_group error in line 1423(before). EDA STUDENT PE… #9

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Update 1 the gender_group error in line 1423(before). EDA STUDENT PE… #9

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Update 1 the gender_group error in line 1423(before). EDA STUDENT PE…
shahpershahin Jun 13, 2023
b94dd67
Update 2. EDA STUDENT PERFORMANCE .ipynb
shahpershahin Jun 13, 2023
273064c
Merge pull request #1 from ShahperShahin/ShahperShahin-patch-1
shahpershahin Jun 13, 2023
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52 changes: 33 additions & 19 deletions notebook/1 . EDA STUDENT PERFORMANCE .ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1412,14 +1412,32 @@
"#### 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,
"id": "526d49f9",
"metadata": {},
"outputs": [],
"source": [
"gender_group = df.groupby('gender').mean()\n",
"gender_group = df.groupby('gender_encoded').mean()\n",
"gender_group"
]
},
Expand All @@ -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()"
]
},
{
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