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Add mod.py and cache files

6ff5630 about 1 year ago

1.32 kB

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import pickle
import os

# --- Step 1: Create sample data ---
data = {
    "Math": [55, 40, 70, 80, 30, 90, 60, 50, 45, 85],
    "English": [60, 35, 75, 70, 25, 95, 55, 65, 50, 80],
    "Science": [50, 45, 65, 85, 35, 90, 60, 55, 40, 75]
}

df = pd.DataFrame(data)

# Define pass/fail: pass if average >= 50
df['Pass'] = (df[['Math', 'English', 'Science']].mean(axis=1) >= 50).astype(int)

# --- Step 2: Split data ---
X = df[['Math', 'English', 'Science']]
y = df['Pass']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# --- Step 3: Train model ---
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# --- Step 4: Test model ---
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

# --- Step 5: Save model to your folder ---
folder_path = r"C:\Users\singh\OneDrive\Desktop\my-ml-spcae"
os.makedirs(folder_path, exist_ok=True)  # create folder if it doesn't exist
file_path = os.path.join(folder_path, "student_model.pkl")

with open(file_path, "wb") as f:
    pickle.dump(model, f)

print(f"Model saved successfully at: {file_path}")


	import pandas as pd
	from sklearn.model_selection import train_test_split
	from sklearn.ensemble import RandomForestClassifier
	from sklearn.metrics import accuracy_score
	import pickle
	import os

	# --- Step 1: Create sample data ---
	data = {
	"Math": [55, 40, 70, 80, 30, 90, 60, 50, 45, 85],
	"English": [60, 35, 75, 70, 25, 95, 55, 65, 50, 80],
	"Science": [50, 45, 65, 85, 35, 90, 60, 55, 40, 75]
	}

	df = pd.DataFrame(data)

	# Define pass/fail: pass if average >= 50
	df['Pass'] = (df[['Math', 'English', 'Science']].mean(axis=1) >= 50).astype(int)

	# --- Step 2: Split data ---
	X = df[['Math', 'English', 'Science']]
	y = df['Pass']

	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

	# --- Step 3: Train model ---
	model = RandomForestClassifier(n_estimators=100, random_state=42)
	model.fit(X_train, y_train)

	# --- Step 4: Test model ---
	y_pred = model.predict(X_test)
	print("Accuracy:", accuracy_score(y_test, y_pred))

	# --- Step 5: Save model to your folder ---
	folder_path = r"C:\Users\singh\OneDrive\Desktop\my-ml-spcae"
	os.makedirs(folder_path, exist_ok=True) # create folder if it doesn't exist
	file_path = os.path.join(folder_path, "student_model.pkl")

	with open(file_path, "wb") as f:
	pickle.dump(model, f)

	print(f"Model saved successfully at: {file_path}")