Created
November 19, 2022 05:36
-
-
Save charanquartz/be2abb58ae41680c84f9c8a0e88376b3 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
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) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
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) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment