Detailed Code Explanation

Step-by-Step Explanation

Step 1: Data Collection

Collecting Online Sales Data:

def collect_online_sales_data(api_url, headers):
    response = requests.get(api_url, headers=headers)
    data = response.json()
    return pd.DataFrame(data)

• This function sends a GET request to the specified API URL with provided headers (for authentication).
• The response is expected to be in JSON format, which is then converted to a Pandas DataFrame.

Collecting POS Data:

def collect_pos_data(db_connection_string, query):
    engine = create_engine(db_connection_string)
    data = pd.read_sql_query(query, engine)
    return data

• This function connects to a database using SQLAlchemy and executes a SQL query to fetch data.
• The result is loaded into a Pandas DataFrame.

Collecting Logistics Data:

def collect_logistics_data(csv_file_path):
    return pd.read_csv(csv_file_path)

• This function reads data from a CSV file into a Pandas DataFrame.

Step 2: Data Cleaning

Cleaning the Data:

def clean_data(df):
    df = df.dropna(subset=['sales', 'date'])
    df['date'] = pd.to_datetime(df['date'])
    df = df.fillna(0)
    return df

• This function handles missing values by dropping rows where ‘sales’ or ‘date’ are missing.
• It converts the ‘date’ column to datetime format.
• Any remaining missing values are filled with 0.

Step 3: Data Analysis

Analyzing Sales:

def analyze_sales(df):
    daily_sales = df.groupby('date').agg({'sales': 'sum'}).reset_index()
    return daily_sales

• This function groups the sales data by ‘date’ and calculates the total sales for each day.
• The result is a DataFrame with daily sales.

Identifying Top-Selling Products:

def get_top_selling_products(df, top_n=10):
    top_products = df.groupby('product_id').agg({'sales': 'sum'}).sort_values(by='sales', ascending=False).head(top_n)
    return top_products

• This function groups the data by ‘product_id’ and sums up the sales for each product.
• It sorts the products by sales in descending order and selects the top N products.

Step 4: Sales Forecasting

SARIMA Forecasting:

def sarima_forecasting(df, order, seasonal_order):
    model = SARIMAX(df['sales'], order=order, seasonal_order=seasonal_order)
    model_fit = model.fit(disp=False)
    forecast = model_fit.forecast(steps=30)
    return forecast

• This function creates a SARIMA model with specified parameters.
• The model is fitted to the sales data, and a forecast is generated for the next 30 days.

Prophet Forecasting:

def prophet_forecasting(df):
    df = df.rename(columns={'date': 'ds', 'sales': 'y'})
    model = Prophet()
    model.fit(df)
    future = model.make_future_dataframe(periods=30)
    forecast = model.predict(future)
    return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

• This function renames the columns to match Prophet’s requirements (‘ds’ for date and ‘y’ for sales).
• A Prophet model is created and fitted to the data.
• A forecast is generated for the next 30 days, including confidence intervals.

Step 5: Inventory and Marketing Recommendations

Inventory Recommendations:

def recommend_inventory_levels(forecast):
    inventory_levels = forecast[['ds', 'yhat']].copy()
    inventory_levels['recommended_inventory'] = inventory_levels['yhat'] * 1.2  # 20% buffer
    return inventory_levels

This function calculates recommended inventory levels by adding a 20% buffer to the forecasted sales.

Marketing Recommendations:

def recommend_marketing_strategies(df):
    low_sales_products = df[df['sales'] < df['sales'].quantile(0.25)]
    high_inventory_products = df[df['inventory'] > df['inventory'].quantile(0.75)]
    target_products = pd.merge(low_sales_products, high_inventory_products, on='product_id')
    return target_products

• This function identifies products with low sales (below the 25th percentile) and high inventory (above the 75th percentile).
• These products are potential targets for marketing campaigns to boost sales.

Main Execution Flow

1. Collecting Data:
   • Data is collected from various sources (online sales, POS, logistics) and combined into a single DataFrame.
2. Cleaning Data:
   • The combined data is cleaned using the clean_data function.
3. Analyzing Data:
   • Daily sales and top-selling products are analyzed.
4. Forecasting Sales:
   • SARIMA and Prophet models are used to forecast sales for the next 30 days.
5. Generating Recommendations:
   • Inventory and marketing recommendations are generated based on the forecasted data.
6. Output Results:
   • The results, including top-selling products, sales forecasts, and recommendations, are printed to the console.