post

Author: meng-kiat

docs/.DS_Store

@@ -36,40 +36,152 @@ In this analysis, I used the [SingStat Table Builder](https://tablebuilder.sings
 ## Preprocessing the Dataset
 ### Outlier Analysis
 
-COVID-19 caused heavy drop in retail sales. We created an indicator feature ['is_covid'] for the machine learning algorithm to identify the COVID-19 period as outliers, ensuring that the model is not misled.
+COVID-19 caused heavy drop in retail sales. We created an indicator feature ['is_covid'] for the machine learning algorithm to identify the COVID-19 period as outliers, ensuring that the model can account for these outliers.
 
 {% highlight ruby %}
+###Adding COVID-19 indicator feature
+df['is_covid'] = df.index.to_series().between('2020-04', '2020-06').astype(int)
 {% endhighlight %}
 
-![](/assets/images/wisconsin/corrplot.png)
-
 ### Feature Engineering
 
 Creating time-step features: 'month', 'quarter' and 'year'.
 
 {% highlight ruby %}
+#Time Step Features
+def create_features(df):
+    """
+    Creates time-step features (month, quarter, year) from the DataFrame's index.
+
+    Args:
+        df (pd.DataFrame): The input DataFrame with a DatetimeIndex.
+
+    Returns:
+        pd.DataFrame: The DataFrame with added time-step features.
+    """
+    df_copy = df.copy()
+
+    # df_copy['hour'] = df_copy.index.hour # Uncomment if needed
+    # df_copy['dayofweek'] = df_copy.index.dayofweek # Uncomment if needed
+    df_copy['month'] = df_copy.index.month
+    df_copy['quarter'] = df_copy.index.quarter
+    df_copy['year'] = df_copy.index.year
+    # df_copy['dayofyear'] = df_copy.index.dayofyear # Uncomment if needed
+
+    return df_copy
+
+df = create_features(df)
 {% endhighlight %}
 
+With time-step features, we can do some visualisations to investigate signs of trends and/or seasonality
+
+![monthboxplot](/assets/images/retailsalessg/monthboxplot.png)
+
 We also created lag features: 'sales_lag_1' and 'sales_lag_2'
 
 {% highlight ruby %}
+#Lag Features
+df['sales_lag_1'] = df['retail_sales_value_estimated'].shift(1)
+df['sales_lag_2'] = df['retail_sales_value_estimated'].shift(2)
 {% endhighlight %}
 
-# Model Building
+We assessed that lag features were unsuitable and worsened prediction results instead.
 
+# Model Building
 ## Time Series Cross Validation
+
+We used the TimeSeriesSplit module to conduct time series cross validation by creating n folds to assess the efficacy of using time series forecasting for retail sales values. This method helps to prevent lookahead bias by repeatedly evaluating a different set of 12 months towards the end of the dataset. 
+
+It should be worth considering that our dataset is quite small, so it will be expected to see natural improvements as 12 datarows worth of data are added after each fold.
+
+We used parameters n_split = 3 & test_size = 12.
+
 {% highlight ruby %}
+tss = TimeSeriesSplit(n_splits = 3, test_size = 12
+                      , gap = 0)
 {% endhighlight %}
 
-![](/assets/images/wisconsin/rf_parameter.png)
+## XGBoost Regression
+We used the XGB Reg module to build our model. 
 
 {% highlight ruby %}
+tss = TimeSeriesSplit(n_splits = 3, test_size = 12
+                      , gap = 0)
+
+fold = 0
+preds = []
+scores = []
+for train_idx, val_idx in tss.split(df):
+    train = df.iloc[train_idx]
+    test = df.iloc[val_idx]
+
+    train = create_features(train)
+    test = create_features(test)
+
+    FEATURES = ['quarter', 'month', 'year','is_covid']
+    TARGET = 'retail_sales_value_estimated'
+
+    X_train = train[FEATURES]
+    y_train = train[TARGET]
+
+    X_test = test[FEATURES]
+    y_test = test[TARGET]
+
+    reg = xgb.XGBRegressor(base_score=0.5, booster='gbtree',    
+                           n_estimators=1000,
+                           early_stopping_rounds=50,
+                           objective='reg:linear',
+                           max_depth=3,
+                           learning_rate=0.01)
+    reg.fit(X_train, y_train,
+            eval_set=[(X_train, y_train), (X_test, y_test)],
+            verbose=100)
+
+    y_pred = reg.predict(X_test)
+    preds.append(y_pred)
+    score = np.sqrt(mean_squared_error(y_test, y_pred))
+    scores.append(score)
 {% endhighlight %}
 
-## XGBoost Regression
-We used the XGB Reg module to build our model. We used parameters 
+## Model initial evaluation
+The RMSE results of the folds were:
+Score across folds: 132.5939
+Fold scores:[147.9255505042701, 138.56629972923642, 111.28990440456352]
+
+The image below shows the model's prediction against the actual data for dates past 2024.
+![prediction1year](/assets/images/retailsalessg/prediction1year.png)
+
+As we can see, the model has generally done well to capture the monthly seasonality of retail sales in SG, capturing the shape without too much of an error.
+
+# Prediction & Visualisation
+## Final Model & Forecasting
+
+{% highlight ruby %}
+#Final Model Building
+
+FEATURES = ['quarter', 'month', 'year', 'is_covid']
+TARGET = 'retail_sales_value_estimated'
+
+X_full = df[FEATURES]
+y_full = df[TARGET]
+{% endhighlight %}
+
+{% highlight ruby %}
+reg = xgb.XGBRegressor(
+    base_score=0.5,
+    booster='gbtree',
+    n_estimators=1000,
+    early_stopping_rounds=50,
+    objective='reg:squarederror',  # use this instead of 'reg:linear' (deprecated)
+    max_depth=3,
+    learning_rate=0.1
+)
+
+reg.fit(X_full, y_full, eval_set=[(X_full, y_full)], verbose=100)
+{% endhighlight %}
 
-# Overall Evaluation
-![](/assets/images/wisconsin/accuracy.png)
+## Tableau Visualisation
+We then visualised the actual & forecasted data in Tableau.
 
 # Conclusion
+Using machine learning models to predict retail sales forecasting showed some promise as seen in the results of the time series cross validation. It is worth acknowledging that this dataset is lacking in volume, both in terms of depth and breadth. There could be more data points, i.e. using weekly or even daily sales for analysis, and there could be more exogenous variables such as promotions, holidays, etc.