{"id":146109,"date":"2025-07-25T11:17:10","date_gmt":"2025-07-25T05:47:10","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146109"},"modified":"2025-07-25T11:17:10","modified_gmt":"2025-07-25T05:47:10","slug":"hours-studied-vs-exam-score-using-deep-learning-ann-model","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/hours-studied-vs-exam-score-using-deep-learning-ann-model\/","title":{"rendered":"Deep Learning Project &#8211; Hours Studied vs Exam Score using ANN Model"},"content":{"rendered":"<h3>Program 1<\/h3>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import 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 Data (Hours Studied vs Exam Score)\r\ndata = {\r\n    'Hours': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\r\n    'Score': [15, 25, 35, 45, 55, 65, 70, 78, 88, 95]\r\n}\r\ndf = pd.DataFrame(data)\r\ndf.shape\r\n\r\n# Feature and Target\r\nX = df[['Hours']] # InDepended variable (Input)\r\ny = df['Score'] # Depended variable (output)\r\ny.head()\r\n\r\n# Normalize Features\r\n# Neural networks work better if data is between 0 and 1.\r\n# MinMaxScaler converts Hours into 0\u20131 range.\r\n# Prevents big numbers from dominating small numbers during training.\r\n# For Example orignal hours 1 --&gt; 0.0  , 10 --&gt; 1.0\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\nX_test\r\n\r\n#  Build ANN Model\r\nmodel = Sequential()\r\nmodel.add(Dense(10, activation='relu', input_shape=(1,))) # Input + Hidden\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=100, verbose=0)\r\n\r\n# epochs=100: Model goes through training data 100 times.\r\n# validation_split=0.2: Use 20% of training data for validation.\r\n# verbose=0: No training output printed.\r\n#The model adjusts weights over epochs to minimize loss.\r\n\r\n#  Make Predictions\r\npredicted_score = model.predict(np.array([[0.8]]))\r\n#predicted_score = model.predict([[0.8]])  # normalized value for 8 hours\r\nprint(f\"Predicted Score for 8 hours of study: {predicted_score[0][0]:.2f}\")\r\n<\/pre>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 import numpy as np # For data handling. import pandas as pd # # For data handling. from sklearn.model_selection import train_test_split # Split data into train and test. from sklearn.preprocessing import MinMaxScaler&#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":[34974,34975,34972,34973,34971,8431,33127,33128,20697],"class_list":["post-146109","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-hours-studied-vs-exam-score","tag-hours-studied-vs-exam-score-project","tag-hours-studied-vs-exam-score-using-ann-model","tag-hours-studied-vs-exam-score-using-deep-learning","tag-hours-studied-vs-exam-score-using-deep-learning-ann-model","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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