Clean Code

Admission Prediction using ML/admission_prediction_using_machine_learning.py

@@ -11,6 +11,7 @@
 """# **Upload and Read file**"""
 
 from google.colab import files
+
 files.upload()
 
 df = pd.read_csv("Admission_Predict_Ver1.1.csv")
@@ -22,7 +23,7 @@
 
 df.columns
 
-df.drop('Serial No.',axis=1,inplace=True)
+df.drop("Serial No.", axis=1, inplace=True)
 
 df.head()
 
@@ -37,39 +38,48 @@
 
 sns.distplot(df.CGPA)
 
-sns.pairplot(df,x_vars=['SOP','GRE Score','TOEFL Score','CGPA'],y_vars=['Chance of Admit '],height=5, aspect=0.8, kind='reg')
+sns.pairplot(
+    df,
+    x_vars=["SOP", "GRE Score", "TOEFL Score", "CGPA"],
+    y_vars=["Chance of Admit "],
+    height=5,
+    aspect=0.8,
+    kind="reg",
+)
 
 
 """# **Creating Model**"""
 
 df.columns
 
-x=df[['GRE Score', 'TOEFL Score', 'CGPA']]
+x = df[["GRE Score", "TOEFL Score", "CGPA"]]
 
-y=df[['Chance of Admit ']]
+y = df[["Chance of Admit "]]
 
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import train_test_split
 import random
 
-x_train, x_test, y_train, y_test =train_test_split(x,y,test_size=0.20,random_state=0)
+x_train, x_test, y_train, y_test = train_test_split(
+    x, y, test_size=0.20, random_state=0
+)
 
 x_train.shape
 
 y_train.shape
 
 linreg = LinearRegression()
-linreg.fit(x_train,y_train)
+linreg.fit(x_train, y_train)
 
 
 """# **Testing and Evaluating the Model**"""
 
-y_pred=linreg.predict(x_test)
+y_pred = linreg.predict(x_test)
 
 y_pred[:7]
 
 y_test.head(7)
 
 from sklearn import metrics
-print(metrics.mean_absolute_error(y_test,y_pred))   #96% prediction
 
+print(metrics.mean_absolute_error(y_test, y_pred))  # 96% prediction