give me jupyter notebook code for this given quest...

Use this as your final Jupyter notebook code.

It is built to:

• read train.csv and test.csv
• clean both train and test
• convert time into numeric seconds
• engineer extra useful features
• train multiple regressors
• choose/blend the best ones using CV R2
• save final predictions to output.csv

python
# =========================
# 1. Import libraries
# =========================
import warnings
warnings.filterwarnings("ignore")

import numpy as np
import pandas as pd

from sklearn.model_selection import KFold, cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.ensemble import (
    RandomForestRegressor,
    ExtraTreesRegressor,
    GradientBoostingRegressor,
    HistGradientBoostingRegressor
)
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler

# =========================
# 2. Read datasets
# =========================
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")

print("Train shape:", train.shape)
print("Test shape :", test.shape)
print("\nTrain columns:\n", list(train.columns))
print("\nTest columns:\n", list(test.columns))

# Clean column names
train.columns = train.columns.str.strip()
test.columns = test.columns.str.strip()

# =========================
# 3. Helper functions
# =========================
def time_to_seconds(x):
    """
    Convert HH:MM:SS to total seconds.
    Handles values like 0:00:45, 6:15:30, etc.
    """
    if pd.isna(x):
        return np.nan
    
    # if already numeric, return as-is
    if isinstance(x, (int, float, np.integer, np.floating)):
        return float(x)
    
    x = str(x).strip()
    if x == "":
        return np.nan
    
    parts = x.split(":")
    try:
        parts = [float(p) for p in parts]
        if len(parts) == 3:
            h, m, s = parts
            return h * 3600 + m * 60 + s
        elif len(parts) == 2:
            m, s = parts
            return m * 60 + s
        elif len(parts) == 1:
            return parts[0]
        else:
            return np.nan
    except:
        return np.nan


def preprocess(df, is_train=True):
    df = df.copy()

    # Detect ID column
    id_col = None
    if "id" in df.columns:
        id_col = "id"
    elif "ID" in df.columns:
        id_col = "ID"

    # Preserve original ID
    original_id = df[id_col].copy() if id_col is not None else pd.Series(range(len(df)))

    # Extract numeric part from ID for modeling
    if id_col is not None:
        df["id_num"] = (
            df[id_col]
            .astype(str)
            .str.extract(r"(\d+)", expand=False)
            .astype(float)
        )

    # Convert time column
    if "time" in df.columns:
        df["time_seconds"] = df["time"].apply(time_to_seconds)

        # Additional time features
        df["time_hours"] = df["time_seconds"] // 3600
        df["time_minutes"] = (df["time_seconds"] % 3600) // 60
        df["time_only_seconds"] = df["time_seconds"] % 60

        df.drop(columns=["time"], inplace=True)

    # Numeric conversion for epoch columns and sensor columns
    for col in df.columns:
        if col not in ["id", "ID"]:
            df[col] = pd.to_numeric(df[col], errors="coerce")

    # Feature engineering from epochs
    if "end_epoch" in df.columns and "start_epoch" in df.columns:
        df["epoch_diff"] = df["end_epoch"] - df["start_epoch"]
        df["start_epoch_mod_day"] = df["start_epoch"] % 86400
        df["end_epoch_mod_day"] = df["end_epoch"] % 86400

    # Row-wise sensor summary stats
    sensor_cols = [c for c in df.columns if c.startswith("value_") and c != "value_19"]
    if len(sensor_cols) > 0:
        df["sensor_mean"] = df[sensor_cols].mean(axis=1)
        df["sensor_std"] = df[sensor_cols].std(axis=1)
        df["sensor_min"] = df[sensor_cols].min(axis=1)
        df["sensor_max"] = df[sensor_cols].max(axis=1)
        df["sensor_median"] = df[sensor_cols].median(axis=1)

    return df, original_id


# =========================
# 4. Preprocess train and test
# =========================
train_processed, train_ids = preprocess(train, is_train=True)
test_processed, test_ids = preprocess(test, is_train=False)

# Target column
target_col = "value_19"
if target_col not in train_processed.columns:
    raise ValueError("value_19 column not found in train.csv")

y = train_processed[target_col].copy()

# Drop target from features
X = train_processed.drop(columns=[target_col], errors="ignore")

# Drop raw ID columns from model features
X = X.drop(columns=["id", "ID"], errors="ignore")
X_test = test_processed.drop(columns=["id", "ID"], errors="ignore")

# Align columns
common_cols = sorted(list(set(X.columns).intersection(set(X_test.columns))))
X = X[common_cols].copy()
X_test = X_test[common_cols].copy()

print("\nFinal training features:", X.shape)
print("Final testing features :", X_test.shape)

# =========================
# 5. Models
# =========================
cv = KFold(n_splits=5, shuffle=True, random_state=42)

models = {
    "extra_trees": Pipeline([
        ("imputer", SimpleImputer(strategy="median")),
        ("model", ExtraTreesRegressor(
            n_estimators=1200,
            max_features="sqrt",
            min_samples_leaf=1,
            random_state=42,
            n_jobs=-1
        ))
    ]),
    
    "random_forest": Pipeline([
        ("imputer", SimpleImputer(strategy="median")),
        ("model", RandomForestRegressor(
            n_estimators=1000,
            max_features=0.8,
            min_samples_leaf=1,
            random_state=42,
            n_jobs=-1
        ))
    ]),
    
    "gradient_boosting": Pipeline([
        ("imputer", SimpleImputer(strategy="median")),
        ("model", GradientBoostingRegressor(
            n_estimators=500,
            learning_rate=0.03,
            max_depth=3,
            subsample=0.9,
            random_state=42
        ))
    ]),
    
    "hist_gb": Pipeline([
        ("imputer", SimpleImputer(strategy="median")),
        ("model", HistGradientBoostingRegressor(
            learning_rate=0.03,
            max_depth=8,
            max_iter=600,
            l2_regularization=0.05,
            random_state=42
        ))
    ]),
    
    "ridge": Pipeline([
        ("imputer", SimpleImputer(strategy="median")),
        ("scaler", StandardScaler()),
        ("model", Ridge(alpha=3.0))
    ])
}

# =========================
# 6. Cross-validation
# =========================
results = []
test_predictions = {}

print("\nModel CV scores (R2):\n")

for name, model in models.items():
    scores = cross_val_score(model, X, y, cv=cv, scoring="r2", n_jobs=-1)
    mean_score = scores.mean()
    std_score = scores.std()

    print(f"{name:18s} -> Mean R2: {mean_score:.5f} | Std: {std_score:.5f}")

    # Fit on full data and predict test
    model.fit(X, y)
    test_pred = model.predict(X_test)

    results.append((name, mean_score))
    test_predictions[name] = test_pred

# Sort by best CV score
results = sorted(results, key=lambda x: x[1], reverse=True)
print("\nSorted leaderboard:")
for r in results:
    print(r)

# =========================
# 7. Blend best models
# =========================
best_score = results[0][1]

# Keep models close to best score
selected_models = [(name, score) for name, score in results if score >= best_score - 0.03]

# If only one model survives, still use it
if len(selected_models) == 0:
    selected_models = [results[0]]

print("\nSelected models for blending:")
for name, score in selected_models:
    print(f"{name}: {score:.5f}")

# Convert negative/very small scores into safe positive weights
weights = np.array([max(score, 0.0001) for _, score in selected_models], dtype=float)
weights = weights / weights.sum()

final_pred = np.zeros(len(X_test))
for (name, score), w in zip(selected_models, weights):
    final_pred += w * test_predictions[name]

# Optional clipping if target values are sensor-like and extreme noise appears
# final_pred = np.clip(final_pred, y.min(), y.max())

# =========================
# 8. Create output.csv
# =========================
# Screenshot note says the output columns should be exactly: ID and value_19
submission = pd.DataFrame({
    "ID": test_ids,
    "value_19": final_pred
})

submission.to_csv("output.csv", index=False)

# Backup file in case platform actually expects lowercase id
submission_lower = submission.rename(columns={"ID": "id"})
submission_lower.to_csv("output_lowercase_id.csv", index=False)

print("\nDone.")
print("Saved: output.csv")
print("Also saved backup: output_lowercase_id.csv")
print("\nPreview:")
print(submission.head())

A couple of important points from your screenshots:

• target to predict is value_19
• clean both train and test
• convert time from HH:MM:SS to numeric
• save final file as output.csv
• I used ID in output.csv because your screenshot note says exact columns should be ID and value_19
• I also saved output_lowercase_id.csv as backup because the sample image shows lowercase id

If your platform rejects output.csv because of header name, change only this part:

python
submission = pd.DataFrame({
    "id": test_ids,
    "value_19": final_pred
})

and save again.