Spaces:
Sleeping
Sleeping
File size: 2,239 Bytes
ac44a3f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
# train.py
import pandas as pd, numpy as np, pickle
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.multioutput import MultiOutputRegressor
from sklearn.metrics import mean_absolute_error
CSV_PATH = "student_allinone_300_padded.csv" # change path if needed
FEATURES = ["Attendance","StudyHours","ParentalSupport","SleepHours",
"ReadingHours","BehaviorScore","PretestScore",
"HomeworkCompletion","Participation"]
TARGETS = ["AssignmentAvg","TestScore"]
def main():
df = pd.read_csv(CSV_PATH).copy()
# === Highly recommended: make targets depend on inputs (if your CSV targets were random) ===
rng = np.random.default_rng(42)
if ("AssignmentAvg" in df.columns) and ("TestScore" in df.columns):
# Always recompute to ensure consistency
df["AssignmentAvg"] = (
df["PretestScore"] * 0.5
+ df["StudyHours"] * 3
+ df["HomeworkCompletion"] * 0.20
+ df["Participation"] * 2
+ rng.integers(-5, 6, size=len(df))
).clip(0, 100).round(2)
df["TestScore"] = (
df["PretestScore"] * 0.6
+ df["Attendance"] * 0.20
+ df["ParentalSupport"] * 3
+ df["SleepHours"] * 2
+ df["ReadingHours"] * 2
+ df["BehaviorScore"] * 2
+ rng.integers(-5, 6, size=len(df))
).clip(0, 100).round(2)
X = df[FEATURES]
y = df[TARGETS]
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, random_state=42)
model = MultiOutputRegressor(RandomForestRegressor(n_estimators=200, random_state=42)).fit(Xtr, ytr)
mae = mean_absolute_error(yte, model.predict(Xte))
print("MAE:", round(mae, 3))
# Save feature bounds so app can clip
feature_mins = X.min().to_dict()
feature_maxs = X.max().to_dict()
with open("student_model.pkl", "wb") as f:
pickle.dump({
"model": model,
"features": FEATURES,
"targets": TARGETS,
"feature_mins": feature_mins,
"feature_maxs": feature_maxs
}, f)
print("Saved student_model.pkl with bounds.")
if __name__ == "__main__":
main() |