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b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/Admission_Pred.py @@ -0,0 +1,26 @@ +#!C:\Users\Lenovo\AppData\Local\Programs\Python\Python37-32\python.exe + +import numpy as np +import pandas as pd +from sklearn.ensemble import GradientBoostingRegressor +from sklearn.svm import SVR +from sklearn.model_selection import train_test_split +import pickle + +# Load and preprocess the data +data = pd.read_csv("Admission_Predict.csv") +X = data.iloc[:, 1:-1].values +y = data.iloc[:, -1].values + + +# Split the data into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) + +# Initialize and train the Gradient Boosting Regressor model +svr_regressor = SVR() +svr_regressor.fit(X_train, y_train) + +pickle.dump(svr_regressor, open('svr_model.pkl', 'wb')) + + + diff --git a/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/Admission_Predict.csv 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But first, please read +. diff --git a/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/README.md b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/README.md new file mode 100644 index 000000000..ca87c39b4 --- /dev/null +++ b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/README.md @@ -0,0 +1,6 @@ +# Forest-Fire-Prediction-Website +A website that predicts the probability of a forest fire taking place based on oxygen,temperature and humidity content +Instructions for Pycharm : +1) In project , add the forest html file in the templates folder +2) In the static folder add the css and js file for css js elements to work on webpage.Get it from here : https://materializecss.com/getting-started.html +3) Complete explanation and tutorial on my youtube channel :https://www.youtube.com/watch?v=Pc8WdnIdXZg diff --git a/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/app.py b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/app.py new file mode 100644 index 000000000..6af6a744c --- /dev/null +++ b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/app.py @@ -0,0 +1,30 @@ +from flask import Flask, request, url_for, redirect, render_template +import pickle +import numpy as np + +app = Flask(__name__) + +# Load your trained Gradient Boosting Regressor model +svr_regressor_model = pickle.load(open('svr_model.pkl', 'rb')) + +@app.route('/') +def hello_world(): + return render_template("Admission_Pred.html") + +@app.route('/predict', methods=['POST', 'GET']) +def predict(): + # Get the input features from the form + input_features = [int(x) for x in request.form.values()] + + # Convert the input features into a numpy array + final_features = np.array(input_features).reshape(1, -1) + + # Predict the probability of forest fire using the trained model + predicted_chance_of_admit = svr_regressor_model.predict(final_features)[0] + + + # Render the template with the prediction result + return render_template('Admission_Pred.html', pred='Probability of Getting Admission at Renowned Institutions: {:.2f}'.format(predicted_chance_of_admit)) + +if __name__ == '__main__': + app.run(debug=True) diff --git a/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/app.yaml b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/app.yaml new file mode 100644 index 000000000..4d8b43e69 --- /dev/null +++ b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/app.yaml @@ -0,0 +1,14 @@ +runtime: python +env: flex +entrypoint: gunicorn -b :$PORT main:application + +runtime_config: + python_version: 3 + + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 0.5 + disk_size_gb: 10 diff --git a/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/forest_fire.py b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/forest_fire.py new file mode 100644 index 000000000..3a695a29d --- /dev/null +++ b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/forest_fire.py @@ -0,0 +1,26 @@ +#!C:\Users\Lenovo\AppData\Local\Programs\Python\Python37-32\python.exe + +import numpy as np +import pandas as pd +from sklearn.ensemble import GradientBoostingRegressor +from sklearn.svm import SVR +from sklearn.model_selection import train_test_split +import pickle + +# Load and preprocess the data +data = pd.read_csv("Admission_Predict.csv") +X = data.iloc[:, 1:-1].values +y = data.iloc[:, -1].values + + +# Split the data into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) + +# Initialize and train the Gradient Boosting Regressor model +svr_regressor = SVR() +svr_regressor.fit(X_train, y_train) + +pickle.dump(svr_regressor, open('svr_model.pkl', 'wb')) + + + diff --git a/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/gb_model.pkl b/React-JS-Projects/Advanced/EduConnect/AdmissionPredict/Admission_Predict_main/gb_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..bd48aa340ac4db8129f97c365f34515e96958c78 GIT binary patch literal 35215 zcmeHQc{o+u`#+}4k`5`lA*5~@x?MvWNrMx)NxGr!44Zv)&OXN(Tr{C8(NAeF085*5)TmCA4J!*Mm--|u;T&-Z!0|E%qKEPL&>*YK|QeSO|{y=(0# z<~2!M5wbxTnQ@T3UHFp3KDTq4xfo5d@)KABluh#5i|Kh9x6t}LLOH_G875jp}C;3 zAd;a#7*d(Ppxj_gf&__}Yze3jkv&|{KSmmjP!~&C{k4H!Js%jl7M~f81ffic6pW1Y zB-E*U4l0yjBtwbNr>f=hg;LPR2$0Pdp-j-TA$79ZLWIc^V$30#%3K_D)sxT?F>#K7 zkBB7{B|Hh0fr7I;YN_iIp&EvYSOQQXgGT634O2ZvZ~;fw6^&n*BH7C#2^mj}3}f=R z9Hwkw@(~W9t}f^&AR~AgVPi}@m}*Z+BH)1{W9*yTja{b@I{4r~5il&0p-LFwh(v?} z>I%WD)cgJQQxw%6N*GyT6{LR#_2k?lySMJo)LqEr1$p;Rt($CS2U$dg-Ctzn=kE>F zW{M!fh;T%T3%P8-g@|j!H)_Hx3B7MVaG$5;tTQo=TuK7|;^M1G;>+I2GPM?Q02_lj zTuj}{dPe9lg+h@a9KirbbD20Gu`XMHgSo;bL-N#dgw11$#j+V0&cy+Zz(gPsfNoNh 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