{"id":146110,"date":"2025-07-25T11:32:40","date_gmt":"2025-07-25T06:02:40","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146110"},"modified":"2025-07-25T11:32:40","modified_gmt":"2025-07-25T06:02:40","slug":"electricity-bill-estimator-using-ann-model","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/electricity-bill-estimator-using-ann-model\/","title":{"rendered":"Deep Learning Project &#8211; Electricity Bill Estimator using ANN Model"},"content":{"rendered":"<h3>Program 1<\/h3>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">We want to predict a person\u2019s electricity bill based on how many units they consumed per month and few additional features like: Units Consumed , No. of People in House , Use of AC (Yes\/No), Use of Electric Geyser (Yes\/No), Use of Solar Panel (Yes\/No)\r\n\r\nimport numpy as np   # For data handling.\r\nimport pandas as pd  #  # For data handling.\r\nfrom sklearn.model_selection import train_test_split  # Split data into train and test.\r\nfrom sklearn.preprocessing import MinMaxScaler  # Normalize the data between 0 and 1.\r\nfrom tensorflow.keras.models import Sequential  # Used to build the Neural Network using Keras.\r\nfrom tensorflow.keras.layers import Dense  # Used to build the Neural Network using Keras.\r\nimport matplotlib.pyplot as plt # To visualize the training process (loss over epochs).\r\n\r\n# Sample Dataset\r\ndata = {\r\n    'Units_Consumed': [150, 300, 450, 600, 750, 900, 1050, 1200, 1350, 1500],\r\n    'People': [2, 3, 4, 5, 2, 3, 4, 5, 2, 3],\r\n    'AC_Used': [1, 1, 1, 0, 1, 1, 0, 0, 1, 1],\r\n    'Geyser_Used': [1, 1, 0, 0, 1, 0, 0, 1, 1, 0],\r\n    'Solar_Panel': [0, 1, 0, 1, 0, 0, 1, 0, 1, 0],\r\n    'Bill_Amount': [500, 1200, 1800, 2200, 3000, 3500, 4000, 4500, 5000, 5500]\r\n}\r\ndf = pd.DataFrame(data)\r\ndf.head()\r\ndf.shape\r\n\r\n# Features and Target\r\nX = df.drop('Bill_Amount', axis=1) # Independed variables Input values\r\ny = df['Bill_Amount'] # Depended variabls (Output)\r\nX.shape\r\ny.shape\r\n\r\n# Normalize data\r\n# Normalize Features\r\n# Neural networks work better if data is between 0 and 1.\r\n# Prevents big numbers from dominating small numbers during training.\r\nscaler = MinMaxScaler()\r\nX_scaled = scaler.fit_transform(X)\r\nX_scaled\r\n\r\n# Split data\r\n# 80% data for training, 20% data for testing.\r\n# random_state=42 ensures reproducibility.\r\nX_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\r\n\r\n# Build ANN Model\r\nmodel = Sequential() # Define ANN Model\r\nmodel.add(Dense(10, activation='relu', input_shape=(X.shape[1],))) # Input + First Hidden Layer\r\n#To allow the model to learn more complex patterns.\r\n#Deep neural networks often perform better with multiple hidden layers.\r\n#First layer may capture basic relationships, second layer may capture higher-order interactions.\r\nmodel.add(Dense(5, activation='relu')) #  Second Hidden Layer\r\nmodel.add(Dense(1))  # Output layer\r\n\r\n# Compile model\r\nmodel.compile(optimizer='adam', loss='mean_squared_error')\r\n\r\n# Train Model\r\nhistory = model.fit(X_train, y_train, validation_split=0.2, epochs=200, verbose=0)\r\n\r\n# Evaluate on test data\r\nloss = model.evaluate(X_test, y_test)\r\nprint(\"Test Loss (MSE):\", loss)\r\n\r\n# Predict example: 1000 units, 3 people, AC used, geyser used, solar panel not used\r\nnew_input = np.array([[1000, 3, 1, 1, 0]])\r\nnew_input_scaled = scaler.transform(new_input)\r\npredicted_bill = model.predict(new_input_scaled)\r\nprint(f\"Predicted Bill Amount: {predicted_bill[0][0]:.2f}\")\r\n<\/pre>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 We want to predict a person\u2019s electricity bill based on how many units they consumed per month and few additional features like: Units Consumed , No. of People in House , Use&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[3653,21686,34976,34980,34977,34978,34979,8431,33127,33128,20697],"class_list":["post-146110","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-deep-learning","tag-deep-learning-project","tag-electricity-bill-estimator","tag-electricity-bill-estimator-project","tag-electricity-bill-estimator-using-ann-model","tag-electricity-bill-estimator-using-deep-learning","tag-electricity-bill-estimator-using-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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