Create app.py

app.py

@@ -0,0 +1,20 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+df = pd.read_csv('/content/drive/MyDrive/Artificial Intelligence/Colab Notebooks/Data/SAT GPA.csv')
+
+from sklearn.model_selection import train_test_split
+
+X = df.drop(['*GPA (4.0 Scale)*'], axis=1)
+y = df['*GPA (4.0 Scale)*']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+from sklearn.linear_model import LinearRegression
+
+model = LinearRegression()
+model.fit(X_train, y_train) 
+
+model.predict([[1600]])