From 42f5334497a89db5d0597e6d54c2b696f93de333 Mon Sep 17 00:00:00 2001 From: Abhik Goswami Date: Thu, 10 Oct 2024 12:12:43 +0530 Subject: [PATCH 1/8] Add retail sales forecasting model and related files --- .../retail_sales_forecasting/data/train.csv | 421571 +++++++++++++++ .../notebooks/Retail_sales_forecasting.ipynb | 342 + .../saved_models/model.pkl | Bin 0 -> 944602 bytes .../saved_models/scaler.pkl | Bin 0 -> 979 bytes 4 files changed, 421913 insertions(+) create mode 100644 models/retail_sales_forecasting/data/train.csv create mode 100644 models/retail_sales_forecasting/notebooks/Retail_sales_forecasting.ipynb create mode 100644 models/retail_sales_forecasting/saved_models/model.pkl create mode 100644 models/retail_sales_forecasting/saved_models/scaler.pkl diff --git a/models/retail_sales_forecasting/data/train.csv b/models/retail_sales_forecasting/data/train.csv new file mode 100644 index 00000000..007e9dc9 --- /dev/null +++ b/models/retail_sales_forecasting/data/train.csv @@ 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matplotlib.pyplot as plt\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c89a3ef5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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StoreDeptDateWeekly_SalesIsHoliday
0112010-02-0524924.50False
1112010-02-1246039.49True
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..................
138112012-09-2818947.81False
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143 rows × 5 columns

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" + ], + "text/plain": [ + " Store Dept Date Weekly_Sales IsHoliday\n", + "0 1 1 2010-02-05 24924.50 False\n", + "1 1 1 2010-02-12 46039.49 True\n", + "2 1 1 2010-02-19 41595.55 False\n", + "3 1 1 2010-02-26 19403.54 False\n", + "4 1 1 2010-03-05 21827.90 False\n", + ".. ... ... ... ... ...\n", + "138 1 1 2012-09-28 18947.81 False\n", + "139 1 1 2012-10-05 21904.47 False\n", + "140 1 1 2012-10-12 22764.01 False\n", + "141 1 1 2012-10-19 24185.27 False\n", + "142 1 1 2012-10-26 27390.81 False\n", + "\n", + "[143 rows x 5 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the data\n", + "data = pd.read_csv(r\"C:\\Users\\Abhik Goswami\\Music\\gssoc\\predictive-calc\\models\\retail_sales_forecasting\\data\\train.csv\", parse_dates=['Date'])\n", + "data = data[(data['Store'] == 1) & (data['Dept'] == 1)] # Focusing on one store and department for simplicity\n", + "\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "49be68eb", + "metadata": {}, + "outputs": [], + "source": [ + "# Feature Engineering\n", + "data['Year'] = data['Date'].dt.year\n", + "data['Month'] = data['Date'].dt.month\n", + "data['WeekOfYear'] = data['Date'].dt.isocalendar().week\n", + "\n", + "# Drop unnecessary columns\n", + "data.drop(['Date'], axis=1, inplace=True)\n", + "\n", + "# Define features and target\n", + "X = data.drop(['Weekly_Sales'], axis=1)\n", + "y = data['Weekly_Sales']\n", + "\n", + "# Split data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=False)\n", + "\n", + "# Normalize the features\n", + "scaler_X = MinMaxScaler()\n", + "scaler_y = MinMaxScaler()\n", + "\n", + "X_train_scaled = scaler_X.fit_transform(X_train)\n", + "X_test_scaled = scaler_X.transform(X_test)\n", + "\n", + "# Reshape y for scaling\n", + "y_train = np.array(y_train).reshape(-1, 1)\n", + "y_test = np.array(y_test).reshape(-1, 1)\n", + "\n", + "y_train_scaled = scaler_y.fit_transform(y_train)\n", + "y_test_scaled = scaler_y.transform(y_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "008c8ae9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Root Mean Squared Error: 7547.514401503674\n", + "Mean Absolute Error: 3215.8597517241383\n" + ] + } + ], + "source": [ + "# Train the Random Forest model\n", + "model = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "model.fit(X_train_scaled, y_train_scaled.ravel()) # Flatten y for model training\n", + "\n", + "# Predictions\n", + "predictions_scaled = model.predict(X_test_scaled)\n", + "predictions = scaler_y.inverse_transform(predictions_scaled.reshape(-1, 1))\n", + "\n", + "# Evaluate the model\n", + "y_test_actual = scaler_y.inverse_transform(y_test_scaled)\n", + "\n", + "rmse = np.sqrt(mean_squared_error(y_test_actual, predictions))\n", + "mae = mean_absolute_error(y_test_actual, predictions)\n", + "\n", + "print(\"Root Mean Squared Error:\", rmse)\n", + "print(\"Mean Absolute Error:\", mae)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "11caa9cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot predictions vs actual\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(y_test_actual, label='Actual Sales', color='blue')\n", + "plt.plot(predictions, label='Predicted Sales', color='red')\n", + "plt.xlabel(\"Test Samples\")\n", + "plt.ylabel(\"Weekly Sales\")\n", + "plt.title(\"Random Forest - Actual vs Predicted Weekly Sales\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ebd9cec0", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'saved_models/model.pkl'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mC:\\Users\\ABHIKG~1\\AppData\\Local\\Temp/ipykernel_8836/3587956355.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Save the trained model as model.pkl\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"saved_models/model.pkl\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"wb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Save the scalers in scaler.pkl\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_models/model.pkl'" + ] + } + ], + "source": [ + "# Save the trained model as model.pkl\n", + "model_path = os.path.join(r\"C:\\Users\\Abhik Goswami\\Music\\gssoc\\predictive-calc\\models\\retail_sales_forecasting\\saved_models\", \"model.pkl\")\n", + "with open(model_path, \"wb\") as f:\n", + " pickle.dump(model, f)\n", + "\n", + "# Save the scalers in scaler.pkl\n", + "scalers = {'scaler_X': scaler_X, 'scaler_y': scaler_y}\n", + "scaler_path = os.path.join(model_folder, \"scaler.pkl\")\n", + "with open(scaler_path, \"wb\") as f:\n", + " pickle.dump(scalers, f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14bc8c0b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + 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HcmV?d00001 From 7e2e17a176900d81d8ffd78de151c0f6135d9b2b Mon Sep 17 00:00:00 2001 From: Abhik Goswami Date: Thu, 10 Oct 2024 12:25:52 +0530 Subject: [PATCH 2/8] Add model.py for retail sales forecasting --- models/retail_sales_forecasting/model.py | 31 ++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 models/retail_sales_forecasting/model.py diff --git a/models/retail_sales_forecasting/model.py b/models/retail_sales_forecasting/model.py new file mode 100644 index 00000000..47b7e31c --- /dev/null +++ b/models/retail_sales_forecasting/model.py @@ -0,0 +1,31 @@ +import pickle +import os +import pandas as pd +import numpy as np + +# Paths to model and scaler +model_path = os.path.join("models", "model.pkl") +scaler_path = os.path.join("models", "scaler.pkl") + +# Load the model and scaler +with open(model_path, "rb") as f: + model = pickle.load(f) + +with open(scaler_path, "rb") as f: + scaler = pickle.load(f) + +def predict(input_data): + """ + Accepts input_data as a dictionary and returns the prediction. + Expected keys: 'store_number', 'department_number', 'date', 'is_holiday' + """ + # Convert date to timestamp and structure data for the model + input_data['date'] = pd.to_datetime(input_data['date']).timestamp() + input_df = pd.DataFrame([input_data]) + + # Scale the input data + input_df_scaled = scaler.transform(input_df) + + # Make the prediction + prediction = model.predict(input_df_scaled) + return prediction[0] From b2eb64eb475319a26d5bffc8696e3c217b60d43d Mon Sep 17 00:00:00 2001 From: Abhik Goswami Date: Thu, 10 Oct 2024 12:26:38 +0530 Subject: [PATCH 3/8] Add predict.py for retail sales forecasting --- models/retail_sales_forecasting/predict.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 models/retail_sales_forecasting/predict.py diff --git a/models/retail_sales_forecasting/predict.py b/models/retail_sales_forecasting/predict.py new file mode 100644 index 00000000..3a5aad86 --- /dev/null +++ b/models/retail_sales_forecasting/predict.py @@ -0,0 +1,18 @@ +from models.retail_sales_forecasting.model import predict + +def get_prediction(store_number, department_number, date, is_holiday): + """ + Prepares the input data and calls the predict function from model.py. + """ + # Prepare the input data as a dictionary + input_data = { + "store_number": store_number, + "department_number": department_number, + "date": date, # Date is in 'YYYY-MM-DD' format + "is_holiday": is_holiday + } + + # Call the predict function + result = predict(input_data) + return result + From 3c70aac9b9f32c39467aa35f460b982e6083e241 Mon Sep 17 00:00:00 2001 From: Abhik Goswami Date: Thu, 10 Oct 2024 12:27:14 +0530 Subject: [PATCH 4/8] Add form config for retail sales forecasting --- form_configs/retail_sales_forecasting.json | 38 ++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 form_configs/retail_sales_forecasting.json diff --git a/form_configs/retail_sales_forecasting.json b/form_configs/retail_sales_forecasting.json new file mode 100644 index 00000000..3efb82e1 --- /dev/null +++ b/form_configs/retail_sales_forecasting.json @@ -0,0 +1,38 @@ +{ + "form_name": "Retail Sales Forecasting", + "fields": [ + { + "label": "Store Number", + "name": "store_number", + "type": "number", + "placeholder": "Enter store number", + "min": 1, + "max": 50, + "default": 1, + "step": 1 + }, + { + "label": "Department Number", + "name": "department_number", + "type": "number", + "placeholder": "Enter department number", + "min": 1, + "max": 100, + "default": 1, + "step": 1 + }, + { + "label": "Date", + "name": "date", + "type": "date", + "placeholder": "Select a date" + }, + { + "label": "Is Holiday", + "name": "is_holiday", + "type": "checkbox", + "placeholder": "Check if it’s a holiday", + "default": false + } + ] +} From 72e3f7411fb4d427295037f607475365ab97011f Mon Sep 17 00:00:00 2001 From: Abhik Goswami Date: Thu, 10 Oct 2024 12:27:54 +0530 Subject: [PATCH 5/8] Updated pages.json for the retail sales forecaster --- pages/pages.json | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/pages/pages.json b/pages/pages.json index 1b412969..51998658 100644 --- a/pages/pages.json +++ b/pages/pages.json @@ -109,6 +109,26 @@ "form_name": "Gold Price Form" } ] + }, + "Retail Sales Forecasting": { + "title": "Retail Sales Forecasting", + "page_title": "Retail Sales Forecasting", + "page_icon": "\ud83d\uded2", + "model_predict_file_path": "models/retail_sales_forecasting/predict.py", + "model_function": "get_prediction", + "model_detail_function": "model_details", + "form_config_path": "form_configs/retail_sales_forecasting.json", + "tabs": [ + { + "name": "Sales Forecasting", + "type": "form", + "form_name": "Retail Sales Forecasting" + }, + { + "name": "Model Details", + "type": "model_details" + } + ] } } From e3752a851283c382bcacb4ad797a8d9796a5467a Mon Sep 17 00:00:00 2001 From: Abhik Goswami Date: Thu, 10 Oct 2024 12:28:28 +0530 Subject: [PATCH 6/8] Add Retail_Sales_Forecaster.py page --- pages/Retail_Sales_Forecaster.py | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 pages/Retail_Sales_Forecaster.py diff --git a/pages/Retail_Sales_Forecaster.py b/pages/Retail_Sales_Forecaster.py new file mode 100644 index 00000000..d980a2d6 --- /dev/null +++ b/pages/Retail_Sales_Forecaster.py @@ -0,0 +1,4 @@ +from page_handler import PageHandler + +page_handler = PageHandler("pages/pages.json") +page_handler.render_page("Retail Sales Forecaster") From 81c9dc7dc0f24ba2f2f565128614be4584843f78 Mon Sep 17 00:00:00 2001 From: Abhik Goswami Date: Thu, 10 Oct 2024 12:55:08 +0530 Subject: [PATCH 7/8] Updated App.py for retail sales forecaster section --- App.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/App.py b/App.py index dd683ffb..068c3c8a 100644 --- a/App.py +++ b/App.py @@ -108,3 +108,35 @@ """ ) +st.write( + "- **Retail Price Forecaster**: Forecast the future sales of a retail store based on time series analyis." +) +# Retail Sales Forecaster Section +with st.expander("Retail Sales Forecaster - More Information"): + st.subheader("Introduction") + st.write( + """ + The Retail Sales Forecaster uses historical sales data and machine learning algorithms to predict future sales for a specific store and department. This tool aims to help businesses optimize inventory management and enhance decision-making based on predicted demand. + """ + ) + + # Dataset section + st.subheader("Retail Sales Dataset") + st.write( + """ + The dataset utilized for this model contains historical sales data, including various features such as date, store ID, department ID, and sales amount. The objective is to forecast future sales based on historical trends and seasonal patterns. + """ + ) + + # Input features section + st.subheader("Additional Variable Information") + st.write( + """ + - **date**: The date of the sales transaction. + - **store_id**: The unique identifier for the store. + - **department_id**: The unique identifier for the department within the store. + - **sales**: The amount of sales made on that day for the specific store and department. + - **is_holiday**: A binary indicator that specifies whether the week includes a holiday (1 for holiday, 0 for non-holiday). + """ + ) + From 4480451529c209b0623228518d444744cc34c853 Mon Sep 17 00:00:00 2001 From: Yashasvini Sharma <100478608+yashasvini121@users.noreply.github.com> Date: Fri, 11 Oct 2024 00:59:16 +0530 Subject: [PATCH 8/8] Fix retail sales forecaster model name in pages.json --- pages/pages.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pages/pages.json b/pages/pages.json index 51998658..9a7b2cd1 100644 --- a/pages/pages.json +++ b/pages/pages.json @@ -110,7 +110,7 @@ } ] }, - "Retail Sales Forecasting": { + "Retail Sales Forecaster": { "title": "Retail Sales Forecasting", "page_title": "Retail Sales Forecasting", "page_icon": "\ud83d\uded2",