How to Use CatBoost to Predict Humidity at M Chinnaswamy Stadium

Humidity forecasts can support pitch preparation, player-comfort planning, broadcast operations, crowd management, and rain-risk decisions at M Chinnaswamy Stadium in Bengaluru. A useful model should do more than fit historical observations: it must respect time order, handle changing weather patterns, and produce predictions that staff can interpret.

This guide explains how to use CatBoost to predict humidity in M Chinnaswamy Stadium using hourly weather data. The same workflow applies to other venue-level forecasting projects and complements broader guidance on implementing scalable machine learning pipelines for predictive analytics.

Define the forecasting problem

First decide what “humidity” means in your project. Relative humidity is usually reported as a percentage, while dew point measures the temperature at which moisture condenses. They are related but not interchangeable.

Specify:

Target: relative humidity in percentage points.
Forecast horizon: for example, one, three, six, or 24 hours ahead.
Granularity: hourly data is generally more useful than daily averages for match operations.
Prediction time: before a match, at innings break, or continuously during an event.
Acceptable error: an MAE of 3 percentage points may be operationally useful, while a research application may require tighter performance.

Avoid using information that would not be available at prediction time. If the goal is a six-hour forecast, the model must not use the humidity recorded six hours in the future or weather fields revised after that timestamp.

Collect and structure Bengaluru weather data

Use a reliable historical source with observations close to the stadium, rather than assuming that a city-wide reading represents conditions inside the venue. Possible inputs include a weather API, an automated station, or a calibrated sensor installed near the ground. Record the source, units, timezone, and measurement interval.

Useful columns include:

Timestamp in Asia/Kolkata time.
Relative humidity and temperature.
Dew point, pressure, wind speed, wind direction, and rainfall.
Cloud cover, solar radiation, and visibility where available.
Forecast values available at the time the prediction is made.
Match or event indicators, including start time, day/night status, and venue occupancy if legally and operationally available.

Store raw data separately from cleaned data. This makes it possible to audit sensor faults, API changes, and unusual readings. A reproducible pipeline matters as much as model choice; teams working on operational forecasting can learn from patterns in predictive analytics solutions for Indian SME spinning mills, where inconsistent industrial data is a common constraint.

Engineer features that reflect humidity dynamics

Humidity follows strong daily and seasonal cycles. CatBoost can learn nonlinear relationships, but it still benefits from features that represent time and recent weather conditions clearly.

Create:

Hour and minute-of-day, represented with sine and cosine transformations.
Month, monsoon-season indicator, weekday, and day/night status.
Lagged humidity, temperature, rainfall, and dew point from 1, 3, 6, 12, and 24 hours earlier.
Rolling mean, minimum, and maximum values over the previous 3, 6, and 24 hours.
Temperature–dew-point spread, which can indicate near-saturation conditions.
Recent rainfall totals and a binary rain indicator.
Wind-direction sectors as categorical values.
Match-day and event-stage features, if they are known before inference.

For a forecast at time *t + 3*, calculate every lag and rolling statistic using observations available at or before *t*. Missing lag values at the beginning of the dataset should remain missing if CatBoost can handle them, or be removed only after checking how much data is lost.

Train a CatBoost regression model

Install the required packages:

pip install catboost pandas scikit-learn matplotlib

A baseline implementation might look like this:

import pandas as pd
from catboost import CatBoostRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error

# Data must be sorted chronologically and engineered without future leakage
frame = pd.read_csv("chinnaswamy_humidity.csv", parse_dates=["timestamp"])
frame = frame.sort_values("timestamp").dropna(subset=["humidity"])

features = [
    "temperature", "wind_speed", "rainfall", "pressure",
    "dew_point", "hour", "month", "humidity_lag_1",
    "humidity_lag_3", "humidity_roll_6"
]
X = frame[features]
y = frame["humidity"]

# Chronological split: do not randomly shuffle time-series observations
cut = int(len(frame) * 0.8)
X_train, X_test = X.iloc[:cut], X.iloc[cut:]
y_train, y_test = y.iloc[:cut], y.iloc[cut:]

model = CatBoostRegressor(
    loss_function="MAE",
    eval_metric="MAE",
    iterations=1500,
    depth=7,
    learning_rate=0.04,
    l2_leaf_reg=5,
    random_seed=42,
    verbose=200
)

model.fit(
    X_train, y_train,
    eval_set=(X_test, y_test),
    early_stopping_rounds=100
)

predictions = model.predict(X_test)
mae = mean_absolute_error(y_test, predictions)
rmse = mean_squared_error(y_test, predictions) ** 0.5
print(f"MAE: {mae:.2f} percentage points")
print(f"RMSE: {rmse:.2f} percentage points")

CatBoost is especially convenient when your dataset includes categorical fields such as wind-direction sector, season, event type, or match session. Pass those columns through cat_features rather than applying arbitrary integer labels. Do not mark continuous measurements as categorical.

Validate against realistic baselines

A model is useful only if it beats simple alternatives. Compare CatBoost with:

The last observed humidity value.
Humidity from the same hour on the previous day.
A rolling mean of recent observations.
A weather-service forecast, if one is available.

Use walk-forward validation: train on an initial period, predict the next block, expand the training window, and repeat. Report MAE and RMSE by month, forecast horizon, daytime, and rainfall condition. A model that performs well in dry months but fails during Bengaluru’s monsoon should not be deployed without safeguards.

Also inspect error plots and bias. Consistent underprediction near 90–100% humidity may matter more operationally than a similar error in the middle of the range. Clip predictions to the physical range of 0–100 only as a final presentation step; investigate why the model produces invalid values rather than hiding the problem.

Interpret and deploy the forecast

CatBoost feature importance can identify influential variables, while SHAP values can show why a particular prediction is high or low. Use these explanations to detect leakage, sensor failures, or implausible relationships. For example, a sudden dominance of an event identifier may indicate that the model has memorised a narrow historical pattern.

For production use:

Version the dataset, feature code, model, and CatBoost parameters.
Log each prediction with its input timestamp and data source.
Monitor missing values, sensor drift, latency, and error after actual humidity arrives.
Retrain on a schedule and after major sensor or API changes.
Provide prediction intervals or alert bands rather than presenting one value as certain.
Keep a fallback rule, such as the latest valid observation, when inputs are stale.

The same discipline used in building predictive maintenance systems with AI applies here: monitor the complete decision pipeline, not just offline accuracy. If the project later expands to multiple venues or weather variables, consider reusable data contracts and model-serving components described in AI predictive maintenance for railway infrastructure assets.

Practical limitations

Stadium microclimates are difficult to model. Roof structures, turf irrigation, crowd density, lighting, nearby buildings, and sensor placement can produce conditions that differ from an airport or city-centre station. Treat external weather data as a starting point, then calibrate with venue-level observations.

CatBoost does not remove the need for sound measurement, leakage controls, or domain review. If only a small amount of local data is available, begin with a transparent baseline and quantify uncertainty. For weather work across Indian cities, related approaches include Bhubaneswar weather prediction with Hugging Face models and Guwahati weather prediction with Hugging Face models.

FAQ

Can CatBoost predict humidity directly?
Yes. Use CatBoostRegressor with humidity as a continuous target. For a humidity category such as low, moderate, or high, use classification instead, but preserve the continuous model for precise operational forecasts.

Should I use a random train-test split?
No for a time-series deployment scenario. Random splitting can place future weather patterns in training data and make performance look better than it will be in practice.

How much data is needed?
At least several months of hourly observations is a reasonable starting point, but a full year or more is preferable because it captures seasonal and monsoon effects. More data cannot compensate for unreliable timestamps or poor sensor placement.

What should I do when humidity observations are missing?
Audit the cause first. Short gaps may be imputed using carefully designed time-series methods, while long outages should be flagged. Never create target values from future observations during training.

Apply for AI Grants India

A venue-weather system can become a broader climate, sports, or infrastructure product when it has a clear user, measurable operational benefit, and reliable deployment plan. Explore funding opportunities at AI Grants India.