charanquartz · November 19, 2022 05:36 · Nov 19, 2022
diff --git a/model train.py b/model train.py
@@ -0,0 +1,71 @@
+import pandas as pd
+
+import pickle
+from sklearn.linear_model import LogisticRegression
+from sklearn import svm
+import matplotlib.pyplot as plt
+
+df=pd.read_csv('marks.csv')
+df.info()
+
+
+df.isnull().sum()
+df.describe()
+df.dropna(axis=0,inplace=True)
+
+df['gender'] = df['gender'].map({'male': 1 ,'female': 2})
+
+
+cdf = df[['gender','internalmarks','internalmarks1','internalmarks2','study_hours','externalmarks']]
+
+x = cdf.iloc[:, :5]
+y = cdf.iloc[:, -1]
+
+
+from sklearn.linear_model import LinearRegression
+linearRegression = LinearRegression()
+linearRegression.fit(x, y)
+lracc = linearRegression.score(x,y)
+
+
+from sklearn.tree import DecisionTreeRegressor
+model = DecisionTreeRegressor(random_state=44)
+model.fit(x, y)
+dtacc = model.score(x,y)
+
+from sklearn.svm import SVR
+SVM = SVR()
+SVM.fit(x, y)
+SVMacc =SVM.score(x, y)
+
+
+print(model.predict([[2,69,90,88,6.56]]))
+
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0, test_size=0.3, shuffle=False)
+SVM.score(X_train, y_train)
+
+svm_acc=round(SVM.score(x,y), 4)
+
+
+
+data = {'LinearRegression':lracc*100, 'SVC':SVMacc*100, 'DecisionTree':dtacc*100}
+courses = list(data.keys())
+values = list(data.values())
+
+fig = plt.figure(figsize = (10, 5))
+
+# creating the bar plot
+plt.bar(courses, values, color =['black', 'red', 'green', 'cyan'], 
+        width = 0.4)
+
+plt.xlabel("Algorithm")
+plt.ylabel("Accuracy")
+plt.title("Accuracy of Algorithms")
+plt.show()
+
+
+file= open('my_modell.pkl','wb')
+pickle.dump(model,file,protocol=2)
+
diff --git a/running flask app.py b/running flask app.py
@@ -0,0 +1,30 @@
+from flask import Flask, request, render_template
+import pickle
+import numpy as np
+
+
+model = pickle.load(open('my_model.pkl', 'rb'))
+
+
+app = Flask(__name__)
+
+
+@app.route('/')
+def hello():
+    return render_template("Hoome.html")
+
+
+@app.route('/predict', methods=['POST'])
+def home():
+    data1 = 1
+    data2 = request.form['a']
+    data3 = request.form['b']
+    data4 = request.form['c']
+    data5 = request.form['d']
+    arr = np.array([[data1, data2, data3, data4, data5]])
+    pred = model.predict(arr)
+    return render_template('After.html', pred=pred)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)