Long Short-Term Memory..ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":9138978,"sourceType":"datasetVersion","datasetId":5519318}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<a href=\"https://colab.research.google.com/github/SURESHBEEKHANI/Long-Short-Term-Memory_LSTM/blob/main/Long_Short_Term_Memory_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>","metadata":{"_uuid":"de774d31-64e8-42fe-a902-12711350dcb3","_cell_guid":"91bd6838-f2a5-4e4f-aa20-ba500f233631","id":"view-in-github","trusted":true}},{"cell_type":"code","source":"# Import the numpy library, which helps with mathematical operations\nimport numpy as np\n\n# Import the tensorflow library, which is used for building and training machine learning models\nimport tensorflow as tf\n\nimport pandas as pd\n\n# Import the Sequential class from tensorflow, which helps in building a sequence of layers for our model\nfrom tensorflow.keras.models import Sequential\n\n# Import the Tokenizer class from tensorflow, which helps to process and prepare text data for our model\nfrom tensorflow.keras.preprocessing.text import Tokenizer\n\n# Import the layers we will use in our model: Dense (fully connected layers), Embedding (for converting words into numerical data), and LSTM (a type of layer useful for understanding sequences)\nfrom tensorflow.keras.layers import Dense, Embedding, LSTM\n\n# Import the pad_sequences function from tensorflow, which helps to make sure all sequences (like sentences) are the same length by adding padding if needed\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences  # Fixed a typo here","metadata":{"_uuid":"5c860ef3-f981-45a4-9b2a-4772c4eabaf5","_cell_guid":"83c97389-61f2-487f-82f2-84f86758d3c0","collapsed":false,"id":"PsWSLW3GlwIY","jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-08-09T08:27:20.873236Z","iopub.execute_input":"2024-08-09T08:27:20.873529Z","iopub.status.idle":"2024-08-09T08:27:20.879459Z","shell.execute_reply.started":"2024-08-09T08:27:20.873503Z","shell.execute_reply":"2024-08-09T08:27:20.878544Z"},"trusted":true},"execution_count":120,"outputs":[]},{"cell_type":"markdown","source":"#  Generate a Large Text Dataset","metadata":{"_uuid":"2646e57c-46bd-4386-8505-b78162c5bd23","_cell_guid":"cb29650d-528f-4860-99ed-6c339190a061","id":"hLVnMTSs_kTh","trusted":true}},{"cell_type":"code","source":"# Define templates for generating text\ntemplates = [\n    \"machine learning is used for {task}\",\n    \"data science involves {technique} and {technique}\",\n    \"deep learning models are trained using {method}\",\n    \"natural language processing helps in {application}\",\n    \"reinforcement learning trains agents using {reward}\",\n    \"supervised learning requires {data_type} data\",\n    \"unsupervised learning finds {pattern} in data\",\n    \"overfitting happens when the model learns {noise}\",\n    \"cross-validation helps to evaluate {model}\",\n    \"feature engineering is important for {task}\"\n]\n\n# Define placeholders for generating sentences\ntasks = [\"classification\", \"regression\", \"clustering\", \"prediction\"]\ntechniques = [\"data cleaning\", \"feature selection\", \"model tuning\", \"data visualization\"]\nmethods = [\"gradient descent\", \"backpropagation\", \"stochastic gradient descent\"]\napplications = [\"text classification\", \"sentiment analysis\", \"language translation\"]\nrewards = [\"positive feedback\", \"negative feedback\", \"rewards\"]\ndata_types = [\"labeled\", \"unlabeled\"]\npatterns = [\"clusters\", \"structures\", \"anomalies\"]\nnoise = [\"irrelevant patterns\", \"outliers\"]\nmodels = [\"neural networks\", \"decision trees\", \"support vector machines\"]\n\n# Generate sentences\ndef generate_sentences(n):\n    sentences = []\n    for _ in range(n):\n        template = np.random.choice(templates)\n        sentence = template.format(\n            task=np.random.choice(tasks),\n            technique=np.random.choice(techniques),\n            method=np.random.choice(methods),\n            application=np.random.choice(applications),\n            reward=np.random.choice(rewards),\n            data_type=np.random.choice(data_types),\n            pattern=np.random.choice(patterns),\n            noise=np.random.choice(noise),\n            model=np.random.choice(models)\n        )\n        sentences.append(sentence)\n    return sentences\n\nlist_text = generate_sentences(1000)\n\n","metadata":{"execution":{"iopub.status.busy":"2024-08-09T08:27:20.880948Z","iopub.execute_input":"2024-08-09T08:27:20.881225Z","iopub.status.idle":"2024-08-09T08:27:21.092821Z","shell.execute_reply.started":"2024-08-09T08:27:20.881202Z","shell.execute_reply":"2024-08-09T08:27:21.091992Z"},"trusted":true},"execution_count":121,"outputs":[]},{"cell_type":"code","source":"print(list_text)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T08:27:21.093826Z","iopub.execute_input":"2024-08-09T08:27:21.094076Z","iopub.status.idle":"2024-08-09T08:27:21.101928Z","shell.execute_reply.started":"2024-08-09T08:27:21.094053Z","shell.execute_reply":"2024-08-09T08:27:21.100901Z"},"trusted":true},"execution_count":122,"outputs":[{"name":"stdout","text":"['supervised learning requires labeled data', 'unsupervised learning finds anomalies in data', 'feature engineering is important for classification', 'feature engineering is important for classification', 'machine learning is used for clustering', 'reinforcement learning trains agents using negative feedback', 'data science involves data cleaning and data cleaning', 'cross-validation helps to evaluate support vector machines', 'deep learning models are trained using stochastic gradient descent', 'deep learning models are trained using backpropagation', 'feature engineering is important for clustering', 'unsupervised learning finds structures in data', 'natural language processing helps in text classification', 'feature engineering is important for prediction', 'cross-validation helps to evaluate decision trees', 'feature engineering is important for prediction', 'reinforcement learning trains agents using negative feedback', 'feature engineering is important for prediction', 'natural language processing helps in text classification', 'supervised learning requires labeled data', 'deep learning models are trained using backpropagation', 'supervised learning requires unlabeled data', 'unsupervised learning finds clusters in data', 'machine learning is used for clustering', 'overfitting happens when the model learns irrelevant patterns', 'deep learning models are trained using backpropagation', 'supervised learning requires labeled data', 'deep learning models are trained using backpropagation', 'overfitting happens when the model learns irrelevant patterns', 'reinforcement learning trains agents using rewards', 'supervised learning requires unlabeled data', 'feature engineering is important for classification', 'unsupervised learning finds structures in data', 'natural language processing helps in sentiment analysis', 'supervised learning requires unlabeled data', 'supervised learning requires unlabeled data', 'machine learning is used for clustering', 'reinforcement learning trains agents using rewards', 'feature engineering is important for classification', 'cross-validation helps to evaluate support vector machines', 'cross-validation helps to evaluate decision trees', 'machine learning is used for regression', 'cross-validation helps to evaluate decision trees', 'reinforcement learning trains agents using positive feedback', 'feature engineering is important for clustering', 'unsupervised learning finds structures in data', 'machine learning is used for regression', 'reinforcement learning trains agents using positive feedback', 'unsupervised learning finds anomalies in data', 'data science involves model tuning and model tuning', 'cross-validation helps to evaluate decision trees', 'overfitting happens when the model learns irrelevant patterns', 'supervised learning requires labeled data', 'unsupervised learning finds anomalies in data', 'natural language processing helps in language translation', 'deep learning models are trained using stochastic gradient descent', 'cross-validation helps to evaluate decision trees', 'overfitting happens when the model learns outliers', 'deep learning models are trained using gradient descent', 'machine learning is used for classification', 'cross-validation helps to evaluate neural networks', 'overfitting happens when the model learns irrelevant patterns', 'cross-validation helps to evaluate support vector machines', 'feature engineering is important for classification', 'data science involves model tuning and model tuning', 'unsupervised learning finds clusters in data', 'natural language processing helps in sentiment analysis', 'cross-validation helps to evaluate decision trees', 'unsupervised learning finds structures in data', 'supervised learning requires labeled data', 'deep learning models are trained using stochastic gradient descent', 'reinforcement learning trains agents using rewards', 'feature engineering is important for clustering', 'natural language processing helps in text classification', 'reinforcement learning trains agents using rewards', 'overfitting happens when the model learns outliers', 'supervised learning requires labeled data', 'feature engineering is important for prediction', 'cross-validation helps to evaluate neural networks', 'feature engineering is important for regression', 'natural language processing helps in sentiment analysis', 'unsupervised learning finds clusters in data', 'machine learning is used for classification', 'reinforcement learning trains agents using positive feedback', 'supervised learning requires unlabeled data', 'deep learning models are trained using backpropagation', 'unsupervised learning finds structures in data', 'machine learning is used for clustering', 'deep learning models are trained using stochastic gradient descent', 'deep learning models are trained using backpropagation', 'unsupervised learning finds structures in data', 'overfitting happens when the model learns outliers', 'deep learning models are trained using stochastic gradient descent', 'cross-validation helps to evaluate support vector machines', 'deep learning models are trained using gradient descent', 'reinforcement learning trains agents using negative feedback', 'feature engineering is important for regression', 'unsupervised learning finds structures in data', 'overfitting happens when the model learns irrelevant patterns', 'unsupervised learning finds structures in data', 'cross-validation helps to evaluate neural networks', 'overfitting happens when the model learns irrelevant patterns', 'cross-validation helps to evaluate neural networks', 'data science involves model tuning and model tuning', 'reinforcement learning trains agents using rewards', 'feature engineering is important for classification', 'supervised learning requires labeled data', 'machine learning is used for prediction', 'cross-validation helps to evaluate support vector machines', 'deep learning models are trained using stochastic gradient descent', 'machine learning is used for prediction', 'unsupervised learning finds structures in data', 'supervised learning requires labeled data', 'reinforcement learning trains agents using negative feedback', 'data science involves data visualization and data visualization', 'natural language processing helps in text classification', 'natural language processing helps in text classification', 'feature engineering is important for prediction', 'cross-validation helps to evaluate support vector machines', 'natural language processing helps in text classification', 'feature engineering is important for clustering', 'data science involves data cleaning and data cleaning', 'data science involves model tuning and model tuning', 'overfitting happens when the model learns irrelevant patterns', 'supervised learning requires labeled data', 'feature engineering is important for clustering', 'cross-validation helps to evaluate support vector machines', 'reinforcement learning trains agents using positive feedback', 'unsupervised learning finds structures in data', 'reinforcement learning trains agents using positive feedback', 'overfitting happens when the model learns outliers', 'unsupervised learning finds clusters in data', 'data science involves model tuning and model tuning', 'deep learning models are trained using gradient descent', 'data science involves data cleaning and data cleaning', 'cross-validation helps to evaluate support vector machines', 'supervised learning requires labeled data', 'natural language processing helps in text classification', 'unsupervised learning finds clusters in data', 'supervised learning requires labeled data', 'deep learning models are trained using gradient descent', 'overfitting happens when the model learns irrelevant patterns', 'overfitting happens when the model learns outliers', 'natural language processing helps in text classification', 'supervised learning requires unlabeled data', 'machine learning is used for prediction', 'natural language processing helps in text classification', 'cross-validation helps to evaluate support vector machines', 'feature engineering is important for classification', 'data science involves feature selection and feature selection', 'data science involves data visualization and data visualization', 'cross-validation helps to evaluate support vector machines', 'data science involves model tuning and model tuning', 'cross-validation helps to evaluate support vector machines', 'cross-validation helps to evaluate decision trees', 'overfitting happens when the model learns irrelevant patterns', 'deep learning models are trained using backpropagation', 'feature engineering is important for regression', 'data science involves feature selection and feature selection', 'feature engineering is important for prediction', 'natural language processing helps in text classification', 'cross-validation helps to evaluate neural networks', 'machine learning is used for classification', 'overfitting happens when the model learns outliers', 'overfitting happens when the model learns outliers', 'reinforcement learning trains agents using negative feedback', 'supervised learning requires labeled data', 'feature engineering is important for classification', 'cross-validation helps to evaluate decision trees', 'machine learning is used for regression', 'unsupervised learning finds clusters in data', 'machine learning is used for regression', 'supervised learning requires unlabeled data', 'supervised learning requires labeled data', 'data science involves data cleaning and data cleaning', 'supervised learning requires unlabeled data', 'deep learning models are trained using gradient descent', 'unsupervised learning finds structures in data', 'machine learning is used for clustering', 'cross-validation helps to evaluate neural networks', 'supervised learning requires unlabeled data', 'reinforcement learning trains agents using rewards', 'data science involves model tuning and model tuning', 'overfitting happens when the model learns irrelevant patterns', 'data science involves data cleaning and data cleaning', 'supervised learning requires unlabeled data', 'cross-validation helps to evaluate support vector machines', 'machine learning is used for prediction', 'natural language processing helps in language translation', 'data science involves model tuning and model tuning', 'cross-validation helps to evaluate decision trees', 'feature engineering is important for prediction', 'supervised learning requires unlabeled data', 'machine learning is used for classification', 'overfitting happens when the model learns outliers', 'supervised learning requires unlabeled data', 'natural language processing helps in language translation', 'machine learning is used for regression', 'unsupervised learning finds structures in data', 'unsupervised learning finds clusters in data', 'reinforcement learning trains agents using negative feedback', 'reinforcement learning trains agents using positive feedback', 'deep learning models are trained using backpropagation', 'machine learning is used for prediction', 'feature engineering is important for classification', 'reinforcement learning trains agents using positive feedback', 'unsupervised learning finds clusters in data', 'feature engineering is important for regression', 'feature engineering is important for classification', 'deep learning models are trained using gradient descent', 'cross-validation helps to evaluate neural networks', 'overfitting happens when the model learns outliers', 'feature engineering is important for clustering', 'data science involves feature selection and feature selection', 'natural language processing helps in sentiment analysis', 'machine learning is used for prediction', 'natural language processing helps in text classification', 'natural language processing helps in language translation', 'unsupervised learning finds structures in data', 'data science involves data visualization and data visualization', 'feature engineering is important for prediction', 'natural language processing helps in language translation', 'feature engineering is important for clustering', 'supervised learning requires unlabeled data', 'feature engineering is important for regression', 'natural language processing helps in text classification', 'overfitting happens when the model learns irrelevant patterns', 'deep learning models are trained using gradient descent', 'unsupervised learning finds clusters in data', 'feature engineering is important for regression', 'feature engineering is important for classification', 'deep learning models are trained using gradient descent', 'reinforcement learning trains agents using negative feedback', 'supervised learning requires labeled data', 'data science involves data cleaning and data cleaning', 'deep learning models are trained using gradient descent', 'deep learning models are trained using stochastic gradient descent', 'data science involves data cleaning and data cleaning', 'cross-validation helps to evaluate decision trees', 'deep learning models are trained using stochastic gradient descent', 'machine learning is used for regression', 'deep learning models are trained using gradient descent', 'overfitting happens when the model learns irrelevant patterns', 'reinforcement learning trains agents using negative feedback', 'machine learning is used for clustering', 'cross-validation helps to evaluate support vector machines', 'machine learning is used for regression', 'cross-validation helps to evaluate support vector machines', 'overfitting happens when the model learns outliers', 'feature engineering is important for regression', 'natural language processing helps in language translation', 'reinforcement learning trains agents using positive feedback', 'cross-validation helps to evaluate support vector machines', 'cross-validation helps to evaluate neural networks', 'cross-validation helps to evaluate decision trees', 'cross-validation helps to evaluate decision trees', 'reinforcement learning trains agents using negative feedback', 'reinforcement learning trains agents using positive feedback', 'natural language processing helps in text classification', 'unsupervised learning finds clusters in data', 'supervised learning requires labeled data', 'data science involves feature selection and feature selection', 'reinforcement learning trains agents using negative feedback', 'feature engineering is important for classification', 'machine learning is used for regression', 'feature engineering is important for regression', 'supervised learning requires labeled data', 'reinforcement learning trains agents using positive feedback', 'deep learning models are trained using gradient descent', 'deep learning models are trained using gradient descent', 'natural language processing helps in sentiment analysis', 'deep learning models are trained using stochastic gradient descent', 'cross-validation helps to evaluate decision trees', 'machine learning is used for prediction', 'overfitting happens when the model learns outliers', 'natural language processing helps in language translation', 'overfitting happens when the model learns irrelevant patterns', 'supervised learning requires labeled data', 'feature engineering is important for clustering', 'cross-validation helps to evaluate neural networks', 'supervised learning requires unlabeled data', 'data science involves feature selection and feature selection', 'feature engineering is important for regression', 'overfitting happens when the model learns outliers', 'overfitting happens when the model learns outliers', 'unsupervised learning finds structures in data', 'supervised learning requires labeled data', 'overfitting happens when the model learns outliers', 'cross-validation helps to evaluate decision trees', 'overfitting happens when the model learns irrelevant patterns', 'unsupervised learning finds anomalies in data', 'overfitting happens when the model learns outliers', 'data science involves data cleaning and data cleaning', 'deep learning models are trained using stochastic gradient descent', 'deep learning models are trained using stochastic gradient descent', 'overfitting happens when the model learns irrelevant patterns', 'unsupervised learning finds structures in data', 'cross-validation helps to evaluate decision trees', 'data science involves model tuning and model tuning', 'overfitting happens when the model learns outliers', 'data science involves model tuning and model tuning', 'cross-validation helps to evaluate support vector machines', 'overfitting happens when the model learns outliers', 'deep learning models are trained using backpropagation', 'data science involves data visualization and data visualization', 'reinforcement learning trains agents using positive feedback', 'overfitting happens when the model learns outliers', 'unsupervised learning finds clusters in data', 'cross-validation helps to evaluate support vector machines', 'unsupervised learning finds anomalies in data', 'unsupervised learning finds structures in data', 'data science involves model tuning and model tuning', 'overfitting happens when the model learns outliers', 'deep learning models are trained using stochastic gradient descent', 'supervised learning requires unlabeled data', 'data science involves data cleaning and data cleaning', 'supervised learning requires unlabeled data', 'natural language processing helps in text classification', 'natural language processing helps in text classification', 'data science involves model tuning and model tuning', 'machine learning is used for clustering', 'supervised learning requires unlabeled data', 'reinforcement learning trains agents using positive feedback', 'feature engineering is important for classification', 'unsupervised learning finds structures in data', 'feature engineering is important for regression', 'natural language processing helps in language translation', 'data science involves feature selection and feature selection', 'cross-validation helps to evaluate decision trees', 'cross-validation helps to evaluate support vector machines', 'overfitting happens when the model learns outliers', 'overfitting happens when the model learns outliers', 'unsupervised learning finds anomalies in data', 'machine learning is used for clustering', 'machine learning is used for clustering', 'unsupervised learning finds structures in data', 'feature engineering is important for prediction', 'overfitting happens when the model learns irrelevant patterns', 'data science involves data cleaning and data cleaning', 'feature engineering is important for clustering', 'cross-validation helps to evaluate neural networks', 'deep learning models are trained using backpropagation', 'data science involves data visualization and data visualization', 'cross-validation helps to evaluate support vector machines', 'overfitting happens when the model learns irrelevant patterns', 'natural language processing helps in sentiment analysis', 'deep learning models are trained using backpropagation', 'cross-validation helps to evaluate decision trees', 'unsupervised learning finds anomalies in data', 'data science involves feature selection and feature selection', 'cross-validation helps to evaluate support vector machines', 'data science involves model tuning and model tuning', 'machine learning is used for classification', 'deep learning models are trained using stochastic gradient descent', 'machine learning is used for clustering', 'natural language processing helps in sentiment analysis', 'cross-validation helps to evaluate neural networks', 'machine learning is used for classification', 'data science involves data visualization and data visualization', 'cross-validation helps to evaluate neural networks', 'cross-validation helps to evaluate decision trees', 'machine learning is used for prediction', 'deep learning models are trained using stochastic gradient descent', 'overfitting happens when the model learns outliers', 'natural language processing helps in sentiment analysis', 'cross-validation helps to evaluate decision trees', 'data science involves data cleaning and data cleaning', 'natural language processing helps in language translation', 'deep learning models are trained using stochastic gradient descent', 'natural language processing helps in language translation', 'reinforcement learning trains agents using negative feedback', 'overfitting happens when the model learns irrelevant patterns', 'data science involves feature selection and feature selection', 'deep learning models are trained using gradient descent', 'natural language processing helps in language translation', 'feature engineering is important for clustering', 'overfitting happens when the model learns outliers', 'reinforcement learning trains agents using negative feedback', 'reinforcement learning trains agents using negative feedback', 'feature engineering is important for classification', 'machine learning is used for clustering', 'machine learning is used for regression', 'reinforcement learning trains agents using negative feedback', 'unsupervised learning finds anomalies in data', 'feature engineering is important for prediction', 'data science involves feature selection and feature selection', 'cross-validation helps to evaluate support vector machines', 'data science involves feature selection and feature selection', 'deep learning models are trained using stochastic gradient descent', 'unsupervised learning finds structures in data', 'unsupervised learning finds anomalies in data', 'overfitting happens when the model learns irrelevant patterns', 'overfitting happens when the model learns outliers', 'machine learning is used for clustering', 'reinforcement learning trains agents using positive feedback', 'reinforcement learning trains agents using negative feedback', 'deep learning models are trained using backpropagation', 'feature engineering is important for classification', 'deep learning models are trained using stochastic gradient descent', 'supervised learning requires unlabeled data', 'cross-validation helps to evaluate decision trees', 'feature engineering is important for prediction', 'feature engineering is important for classification', 'unsupervised learning finds structures in data', 'overfitting happens when the model learns irrelevant patterns', 'data science involves feature selection and feature selection', 'reinforcement learning trains agents using rewards', 'supervised learning requires labeled data', 'machine learning is used for classification', 'cross-validation helps to evaluate neural networks', 'cross-validation helps to evaluate decision trees', 'unsupervised learning finds structures in data', 'overfitting happens when the model learns outliers', 'overfitting happens when the model learns outliers', 'supervised learning requires labeled data', 'unsupervised learning finds clusters in data', 'reinforcement learning trains agents using rewards', 'cross-validation helps to evaluate support vector machines', 'machine learning is used for prediction', 'overfitting happens when the model learns outliers', 'reinforcement learning trains agents using negative feedback', 'reinforcement learning trains agents using negative feedback', 'deep learning models are trained using backpropagation', 'reinforcement learning trains agents using positive feedback', 'overfitting happens when the model learns outliers', 'feature engineering is important for regression', 'supervised learning requires unlabeled data', 'machine learning is used for clustering', 'natural language processing helps in sentiment analysis', 'cross-validation helps to evaluate decision trees', 'unsupervised learning finds clusters in data', 'natural language processing helps in text classification', 'unsupervised learning finds clusters in data', 'natural language processing helps in language translation', 'unsupervised learning finds anomalies in data', 'natural language processing helps in language translation', 'machine learning is used for classification', 'reinforcement learning trains agents using rewards', 'data science involves data visualization and data visualization', 'unsupervised learning finds structures in data', 'feature engineering is important for clustering', 'data science involves feature selection and feature selection', 'supervised learning requires labeled data', 'supervised learning requires unlabeled data', 'overfitting happens when the model learns irrelevant patterns', 'cross-validation helps to evaluate neural networks', 'deep learning models are trained using gradient descent', 'cross-validation helps to evaluate neural networks', 'cross-validation helps to evaluate neural networks', 'natural language processing helps in sentiment analysis', 'cross-validation helps to evaluate decision trees', 'natural language processing helps in language translation', 'deep learning models are trained using backpropagation', 'machine learning is used for clustering', 'data science involves data cleaning and data cleaning', 'deep learning models are trained using stochastic gradient descent', 'deep learning models are trained using gradient descent', 'reinforcement learning trains agents using rewards', 'deep learning models are trained using backpropagation', 'machine learning is used for regression', 'feature engineering is important for clustering', 'deep learning models are trained using gradient descent', 'deep learning models are trained using stochastic gradient descent', 'unsupervised learning finds anomalies in data', 'reinforcement learning trains agents using rewards', 'overfitting happens when the model learns outliers', 'machine learning is used for clustering', 'natural language processing helps in text classification', 'data science involves model tuning and model tuning', 'overfitting happens when the model learns irrelevant patterns', 'data science involves model tuning and model tuning', 'deep learning models are trained using backpropagation', 'deep learning models are trained using stochastic gradient descent', 'feature engineering is important for clustering', 'data science involves model tuning and model tuning', 'deep learning models are trained using stochastic gradient descent', 'cross-validation helps to evaluate neural networks', 'data science involves data cleaning and data cleaning', 'natural language processing helps in language translation', 'data science involves model tuning and model tuning', 'supervised learning requires unlabeled data', 'overfitting happens when the model learns irrelevant patterns', 'cross-validation helps to evaluate decision trees', 'data science involves model tuning and model tuning', 'reinforcement learning trains agents using rewards', 'machine learning is used for classification', 'reinforcement learning trains agents using rewards', 'machine learning is used for classification', 'supervised learning requires unlabeled data', 'data science involves data cleaning and data cleaning', 'data science involves feature selection and feature selection', 'feature engineering is important for clustering', 'unsupervised learning finds clusters in data', 'machine learning is used for prediction', 'deep learning models are trained using gradient descent', 'natural language processing helps in language translation', 'data science involves feature selection and feature selection', 'cross-validation helps to evaluate decision trees', 'data science involves data visualization and data visualization', 'machine learning is used for classification', 'overfitting happens when the model learns irrelevant patterns', 'data science involves feature selection and feature selection', 'machine learning is used for regression', 'machine learning is used for classification', 'supervised learning requires labeled data', 'deep learning models are trained using gradient descent', 'reinforcement learning trains agents using positive feedback', 'feature engineering is important for regression', 'feature engineering is important for regression', 'machine learning is used for prediction', 'unsupervised learning finds structures in data', 'feature engineering is important for prediction', 'cross-validation helps to evaluate support vector machines', 'data science involves data cleaning and data cleaning', 'reinforcement learning trains agents using positive feedback', 'feature engineering is important for classification', 'machine learning is used for prediction', 'reinforcement learning trains agents using rewards', 'cross-validation helps to evaluate support vector machines', 'deep learning models are trained using backpropagation', 'natural language processing helps in language translation', 'reinforcement learning trains agents using negative feedback', 'cross-validation helps to evaluate decision trees', 'overfitting happens when the model learns irrelevant patterns', 'deep learning models are trained using stochastic gradient descent', 'machine learning is used for classification', 'data science involves data visualization and data visualization', 'overfitting happens when the model learns outliers', 'cross-validation helps to evaluate decision trees', 'machine learning is used for regression', 'overfitting happens when the model learns irrelevant patterns', 'feature engineering is important for clustering', 'data science involves data visualization and data visualization', 'unsupervised learning finds structures in data', 'supervised learning requires unlabeled data', 'natural language processing helps in language translation', 'supervised learning requires labeled data', 'overfitting happens when the model learns outliers', 'deep learning models are trained using backpropagation', 'natural language processing helps in language translation', 'supervised learning requires labeled data', 'overfitting happens when the model learns outliers', 'unsupervised learning finds clusters in data', 'natural language processing helps in sentiment analysis', 'supervised learning requires labeled data', 'cross-validation helps to evaluate decision trees', 'natural language processing helps in sentiment analysis', 'natural language processing helps in text classification', 'data science involves data visualization and data visualization', 'unsupervised learning finds structures in data', 'natural language processing helps in text classification', 'unsupervised learning finds clusters in data', 'unsupervised learning finds anomalies in data', 'natural language processing helps in language translation', 'data science involves data visualization and data visualization', 'unsupervised learning finds structures in data', 'unsupervised learning finds anomalies in data', 'data science involves feature selection and feature selection', 'reinforcement learning trains agents using positive feedback', 'deep learning models are trained using backpropagation', 'reinforcement learning trains agents using rewards', 'deep learning models are trained using stochastic gradient descent', 'machine learning is used for classification', 'cross-validation helps to evaluate neural networks', 'overfitting happens when the model learns irrelevant patterns', 'overfitting happens when the model learns outliers', 'machine learning is used for clustering', 'supervised learning requires labeled data', 'cross-validation helps to evaluate support vector machines', 'natural language processing helps in language translation', 'unsupervised learning finds structures in data', 'deep learning models are trained using gradient descent', 'machine learning is used for prediction', 'supervised learning requires labeled data', 'supervised learning requires labeled data', 'overfitting happens when the model learns outliers', 'cross-validation helps to evaluate neural networks', 'supervised learning requires labeled data', 'deep learning models are trained using gradient descent', 'reinforcement learning trains agents using positive feedback', 'supervised learning requires labeled data', 'unsupervised learning finds clusters in data', 'data science involves model tuning and model tuning', 'natural language processing helps in sentiment analysis', 'supervised learning requires labeled data', 'feature engineering is important for regression', 'natural language processing helps in sentiment analysis', 'supervised learning requires unlabeled data', 'machine learning is used for prediction', 'deep learning models are trained using backpropagation', 'machine learning is used for regression', 'data science involves model tuning and model tuning', 'overfitting happens when the model learns outliers', 'natural language processing helps in sentiment analysis', 'natural language processing helps in language translation', 'cross-validation helps to evaluate neural networks', 'machine learning is used for prediction', 'cross-validation helps to evaluate decision trees', 'reinforcement learning trains agents using positive feedback', 'unsupervised learning finds anomalies in data', 'unsupervised learning finds clusters in data', 'overfitting happens when the model learns outliers', 'supervised learning requires unlabeled data', 'overfitting happens when the model learns irrelevant patterns', 'unsupervised learning finds clusters in data', 'supervised learning requires labeled data', 'machine learning is used for classification', 'cross-validation helps to evaluate neural networks', 'machine learning is used for clustering', 'machine learning is used for clustering', 'natural language processing helps in sentiment analysis', 'deep learning models are trained using gradient descent', 'machine learning is used for prediction', 'feature engineering is important for regression', 'reinforcement learning trains agents using rewards', 'reinforcement learning trains agents using rewards', 'reinforcement learning trains agents using rewards', 'data science involves feature selection and feature selection', 'natural language processing helps in text classification', 'natural language processing helps in language translation', 'reinforcement learning trains agents using positive feedback', 'data science involves model tuning and model tuning', 'cross-validation helps to evaluate support vector machines', 'feature engineering is important for regression', 'deep learning models are trained using gradient descent', 'machine learning is used for regression', 'supervised learning requires unlabeled data', 'deep learning models are trained using stochastic gradient descent', 'feature engineering is important for regression', 'reinforcement learning trains agents using negative feedback', 'deep learning models are trained using stochastic gradient descent', 'unsupervised learning finds clusters in data', 'unsupervised learning finds structures in data', 'machine learning is used for classification', 'overfitting happens when the model learns irrelevant patterns', 'supervised learning requires labeled data', 'deep learning models are trained using stochastic gradient descent', 'overfitting happens when the model learns outliers', 'natural language processing helps in sentiment analysis', 'supervised learning requires labeled data', 'cross-validation helps to evaluate decision trees', 'supervised learning requires labeled data', 'supervised learning requires unlabeled data', 'machine learning is used for classification', 'data science involves model tuning and model tuning', 'overfitting happens when the model learns outliers', 'supervised learning requires unlabeled data', 'supervised learning requires labeled data', 'feature engineering is important for clustering', 'data science involves data visualization and data visualization', 'cross-validation helps to evaluate support vector machines', 'natural language processing helps in sentiment analysis', 'natural language processing helps in sentiment analysis', 'feature engineering is important for classification', 'unsupervised learning finds structures in data', 'machine learning is used for classification', 'natural language processing helps in language translation', 'supervised learning requires unlabeled data', 'natural language processing helps in text classification', 'data science involves data visualization and data visualization', 'feature engineering is important for classification', 'unsupervised learning finds anomalies in data', 'supervised learning requires unlabeled data', 'machine learning is used for classification', 'overfitting happens when the model learns outliers', 'data science involves feature selection and feature selection', 'machine learning is used for classification', 'data science involves feature selection and feature selection', 'unsupervised learning finds clusters in data', 'cross-validation helps to evaluate neural networks', 'data science involves data visualization and data visualization', 'data science involves data visualization and data visualization', 'data science involves feature selection and feature selection', 'cross-validation helps to evaluate neural networks', 'natural language processing helps in language translation', 'machine learning is used for classification', 'unsupervised learning finds structures in data', 'supervised learning requires labeled data', 'deep learning models are trained using backpropagation', 'data science involves data cleaning and data cleaning', 'machine learning is used for regression', 'natural language processing helps in text classification', 'unsupervised learning finds clusters in data', 'supervised learning requires unlabeled data', 'unsupervised learning finds structures in data', 'natural language processing helps in text classification', 'overfitting happens when the model learns outliers', 'data science involves model tuning and model tuning', 'machine learning is used for prediction', 'deep learning models are trained using stochastic gradient descent', 'data science involves data cleaning and data cleaning', 'reinforcement learning trains agents using rewards', 'data science involves feature selection and feature selection', 'natural language processing helps in text classification', 'data science involves model tuning and model tuning', 'supervised learning requires labeled data', 'reinforcement learning trains agents using rewards', 'natural language processing helps in language translation', 'supervised learning requires unlabeled data', 'supervised learning requires labeled data', 'supervised learning requires unlabeled data', 'overfitting happens when the model learns outliers', 'cross-validation helps to evaluate decision trees', 'reinforcement learning trains agents using positive feedback', 'cross-validation helps to evaluate neural networks', 'machine learning is used for prediction', 'deep learning models are trained using backpropagation', 'supervised learning requires labeled data', 'unsupervised learning finds structures in data', 'unsupervised learning finds structures in data', 'supervised learning requires unlabeled data', 'deep learning models are trained using backpropagation', 'feature engineering is important for classification', 'cross-validation helps to evaluate support vector machines', 'feature engineering is important for prediction', 'cross-validation helps to evaluate support vector machines', 'cross-validation helps to evaluate neural networks', 'overfitting happens when the model learns outliers', 'cross-validation helps to evaluate neural networks', 'cross-validation helps to evaluate support vector machines', 'deep learning models are trained using gradient descent', 'reinforcement learning trains agents using positive feedback', 'natural language processing helps in language translation', 'overfitting happens when the model learns outliers', 'cross-validation helps to evaluate neural networks', 'data science involves feature selection and feature selection', 'data science involves data visualization and data visualization', 'cross-validation helps to evaluate support vector machines', 'machine learning is used for clustering', 'data science involves data visualization and data visualization', 'cross-validation helps to evaluate support vector machines', 'reinforcement learning trains agents using positive feedback', 'supervised learning requires labeled data', 'deep learning models are trained using backpropagation', 'machine learning is used for clustering', 'feature engineering is important for prediction', 'feature engineering is important for classification', 'unsupervised learning finds anomalies in data', 'feature engineering is important for classification', 'supervised learning requires unlabeled data', 'feature engineering is important for clustering', 'feature engineering is important for regression', 'feature engineering is important for clustering', 'natural language processing helps in sentiment analysis', 'data science involves data visualization and data visualization', 'machine learning is used for regression', 'supervised learning requires unlabeled data', 'data science involves data visualization and data visualization', 'unsupervised learning finds clusters in data', 'natural language processing helps in sentiment analysis', 'feature engineering is important for prediction', 'machine learning is used for prediction', 'reinforcement learning trains agents using positive feedback', 'unsupervised learning finds anomalies in data', 'machine learning is used for clustering', 'deep learning models are trained using stochastic gradient descent', 'cross-validation helps to evaluate decision trees', 'deep learning models are trained using gradient descent', 'machine learning is used for clustering', 'data science involves data cleaning and data cleaning', 'natural language processing helps in text classification', 'overfitting happens when the model learns irrelevant patterns', 'deep learning models are trained using gradient descent', 'overfitting happens when the model learns outliers', 'overfitting happens when the model learns outliers', 'overfitting happens when the model learns outliers', 'reinforcement learning trains agents using negative feedback', 'reinforcement learning trains agents using rewards', 'supervised learning requires unlabeled data', 'feature engineering is important for clustering', 'machine learning is used for clustering', 'natural language processing helps in language translation', 'data science involves feature selection and feature selection', 'unsupervised learning finds structures in data', 'overfitting happens when the model learns irrelevant patterns', 'feature engineering is important for prediction', 'data science involves data visualization and data visualization', 'data science involves feature selection and feature selection', 'cross-validation helps to evaluate neural networks', 'cross-validation helps to evaluate support vector machines', 'reinforcement learning trains agents using positive feedback', 'data science involves data visualization and data visualization', 'unsupervised learning finds clusters in data', 'supervised learning requires unlabeled data', 'feature engineering is important for classification', 'supervised learning requires unlabeled data', 'supervised learning requires labeled data', 'reinforcement learning trains agents using negative feedback', 'deep learning models are trained using stochastic gradient descent', 'supervised learning requires labeled data', 'deep learning models are trained using gradient descent', 'cross-validation helps to evaluate decision trees', 'supervised learning requires labeled data', 'unsupervised learning finds clusters in data', 'deep learning models are trained using stochastic gradient descent', 'data science involves model tuning and model tuning', 'supervised learning requires labeled data', 'cross-validation helps to evaluate decision trees', 'reinforcement learning trains agents using negative feedback', 'reinforcement learning trains agents using negative feedback', 'unsupervised learning finds structures in data', 'cross-validation helps to evaluate decision trees', 'machine learning is used for classification', 'data science involves model tuning and model tuning', 'natural language processing helps in text classification', 'feature engineering is important for classification', 'natural language processing helps in text classification', 'deep learning models are trained using gradient descent', 'overfitting happens when the model learns irrelevant patterns', 'reinforcement learning trains agents using positive feedback', 'natural language processing helps in language translation', 'supervised learning requires unlabeled data', 'feature engineering is important for clustering', 'machine learning is used for clustering', 'unsupervised learning finds structures in data', 'reinforcement learning trains agents using rewards', 'data science involves data visualization and data visualization', 'machine learning is used for classification', 'natural language processing helps in text classification', 'supervised learning requires unlabeled data', 'natural language processing helps in sentiment analysis', 'deep learning models are trained using backpropagation', 'data science involves feature selection and feature selection', 'feature engineering is important for clustering', 'supervised learning requires labeled data', 'cross-validation helps to evaluate neural networks', 'machine learning is used for regression', 'deep learning models are trained using gradient descent', 'supervised learning requires labeled data', 'deep learning models are trained using gradient descent', 'unsupervised learning finds anomalies in data', 'cross-validation helps to evaluate neural networks', 'machine learning is used for regression', 'cross-validation helps to evaluate neural networks', 'overfitting happens when the model learns irrelevant patterns', 'machine learning is used for clustering', 'supervised learning requires unlabeled data', 'deep learning models are trained using gradient descent', 'reinforcement learning trains agents using negative feedback', 'feature engineering is important for regression', 'reinforcement learning trains agents using rewards', 'unsupervised learning finds structures in data', 'unsupervised learning finds anomalies in data', 'machine learning is used for clustering', 'deep learning models are trained using gradient descent', 'deep learning models are trained using stochastic gradient descent', 'data science involves feature selection and feature selection', 'natural language processing helps in language translation', 'data science involves data visualization and data visualization', 'deep learning models are trained using stochastic gradient descent', 'unsupervised learning finds clusters in data', 'reinforcement learning trains agents using rewards', 'data science involves feature selection and feature selection', 'deep learning models are trained using gradient descent', 'feature engineering is important for prediction', 'machine learning is used for regression', 'unsupervised learning finds structures in data', 'supervised learning requires labeled data', 'unsupervised learning finds clusters in data', 'natural language processing helps in text classification', 'deep learning models are trained using backpropagation', 'overfitting happens when the model learns irrelevant patterns', 'overfitting happens when the model learns outliers', 'natural language processing helps in text classification', 'reinforcement learning trains agents using rewards', 'overfitting happens when the model learns irrelevant patterns', 'machine learning is used for regression', 'unsupervised learning finds structures in data', 'supervised learning requires labeled data', 'feature engineering is important for classification', 'natural language processing helps in text classification', 'cross-validation helps to evaluate neural networks', 'data science involves model tuning and model tuning', 'machine learning is used for classification', 'overfitting happens when the model learns irrelevant patterns', 'machine learning is used for prediction', 'unsupervised learning finds anomalies in data', 'feature engineering is important for regression', 'natural language processing helps in sentiment analysis', 'data science involves data visualization and data visualization', 'supervised learning requires labeled data', 'feature engineering is important for clustering', 'deep learning models are trained using backpropagation', 'feature engineering is important for prediction', 'cross-validation helps to evaluate support vector machines', 'feature engineering is important for regression', 'feature engineering is important for prediction', 'supervised learning requires labeled data', 'overfitting happens when the model learns irrelevant patterns', 'overfitting happens when the model learns outliers', 'data science involves data visualization and data visualization', 'cross-validation helps to evaluate neural networks', 'supervised learning requires unlabeled data', 'unsupervised learning finds clusters in data', 'machine learning is used for classification', 'feature engineering is important for regression', 'machine learning is used for regression', 'cross-validation helps to evaluate support vector machines', 'machine learning is used for classification', 'cross-validation helps to evaluate support vector machines', 'overfitting happens when the model learns outliers', 'machine learning is used for classification', 'cross-validation helps to evaluate decision trees', 'overfitting happens when the model learns outliers', 'reinforcement learning trains agents using negative feedback', 'feature engineering is important for prediction', 'cross-validation helps to evaluate neural networks', 'overfitting happens when the model learns irrelevant patterns', 'deep learning models are trained using stochastic gradient descent', 'supervised learning requires labeled data', 'overfitting happens when the model learns irrelevant patterns', 'unsupervised learning finds clusters in data', 'machine learning is used for classification', 'data science involves model tuning and model tuning', 'supervised learning requires unlabeled data', 'natural language processing helps in sentiment analysis', 'cross-validation helps to evaluate support vector machines', 'overfitting happens when the model learns irrelevant patterns', 'supervised learning requires labeled data', 'feature engineering is important for clustering', 'supervised learning requires labeled data', 'reinforcement learning trains agents using rewards', 'supervised learning requires labeled data', 'deep learning models are trained using backpropagation', 'natural language processing helps in sentiment analysis', 'cross-validation helps to evaluate decision trees', 'reinforcement learning trains agents using positive feedback', 'unsupervised learning finds structures in data', 'unsupervised learning finds anomalies in data', 'feature engineering is important for regression', 'machine learning is used for regression', 'reinforcement learning trains agents using positive feedback', 'cross-validation helps to evaluate neural networks', 'overfitting happens when the model learns outliers', 'unsupervised learning finds structures in data', 'deep learning models are trained using stochastic gradient descent', 'feature engineering is important for prediction', 'natural language processing helps in language translation', 'deep learning models are trained using gradient descent', 'data science involves feature selection and feature selection', 'deep learning models are trained using stochastic gradient descent', 'unsupervised learning finds anomalies in data', 'overfitting happens when the model learns irrelevant patterns', 'cross-validation helps to evaluate decision trees', 'natural language processing helps in text classification', 'feature engineering is important for regression', 'feature engineering is important for classification', 'cross-validation helps to evaluate neural networks', 'cross-validation helps to evaluate decision trees', 'deep learning models are trained using stochastic gradient descent', 'feature engineering is important for regression', 'supervised learning requires labeled data', 'supervised learning requires unlabeled data', 'reinforcement learning trains agents using negative feedback', 'deep learning models are trained using backpropagation', 'feature engineering is important for clustering', 'feature engineering is important for prediction', 'unsupervised learning finds anomalies in data', 'unsupervised learning finds anomalies in data', 'natural language processing helps in text classification', 'data science involves data visualization and data visualization', 'feature engineering is important for regression', 'reinforcement learning trains agents using positive feedback', 'overfitting happens when the model learns irrelevant patterns', 'machine learning is used for prediction', 'unsupervised learning finds structures in data', 'deep learning models are trained using backpropagation', 'supervised learning requires unlabeled data', 'supervised learning requires unlabeled data', 'feature engineering is important for regression', 'supervised learning requires unlabeled data', 'overfitting happens when the model learns irrelevant patterns', 'supervised learning requires unlabeled data', 'cross-validation helps to evaluate support vector machines', 'natural language processing helps in language translation', 'natural language processing helps in text classification', 'data science involves data visualization and data visualization', 'data science involves model tuning and model tuning', 'cross-validation helps to evaluate support vector machines', 'machine learning is used for regression', 'reinforcement learning trains agents using rewards', 'supervised learning requires unlabeled data', 'natural language processing helps in language translation', 'feature engineering is important for classification', 'data science involves feature selection and feature selection', 'supervised learning requires labeled data', 'data science involves feature selection and feature selection', 'machine learning is used for regression', 'machine learning is used for classification', 'overfitting happens when the model learns outliers', 'unsupervised learning finds structures in data', 'supervised learning requires unlabeled data', 'natural language processing helps in sentiment analysis', 'deep learning models are trained using backpropagation', 'unsupervised learning finds anomalies in data', 'natural language processing helps in language translation', 'natural language processing helps in text classification', 'machine learning is used for clustering', 'cross-validation helps to evaluate neural networks', 'cross-validation helps to evaluate neural networks']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Tokenization and Preprocessing data\n\nTokenization is the process of breaking text into smaller units (words, sentences, etc.). Preprocessing involves cleaning and normalizing text by steps such as lowercasing, removing punctuation and stop words, and stemming or lemmatizing words. Together, these steps prepare text data for analysis or machine learning.","metadata":{"_uuid":"f5e3260c-5a0f-4d03-b860-1e44faf8c707","_cell_guid":"34580fb8-f36d-47d0-a15e-6cc2b5e70669","id":"rWUM4bFh_oGo","trusted":true}},{"cell_type":"code","source":"\n# Initialize the Tokenizer object to preprocess and vectorize text data\ntokenizer = Tokenizer()\n\n# Fit the Tokenizer on the list of sentences (or texts) to build the vocabulary\ntokenizer.fit_on_texts(list_text)\n\n# Get the total number of unique words in the vocabulary, and assign and index value\ntotal_words = len(tokenizer.word_index) + 1\n\n# Print the total number of unique words in the vocabulary\nprint(total_words)\n\n# Print the word index dictionary that maps words to their integer index\nprint(tokenizer.word_index)\n\n\n# Initialize an empty list to hold the input sequences\ninput_sequence = []\n\n# Iterate through each sentence in the list of sentences\nfor line in list_text:\n    # Convert each sentence into a sequence of integers using the tokenizer\n    # The 'texts_to_sequences' method converts words to their corresponding integer indices\n    token_list = tokenizer.texts_to_sequences([line])[0]\n    for i in range(1, len(token_list) + 1):  # Use len(token_list) + 1 to include the last element\n        n_gram_sequence = token_list[:i]\n        input_sequence.append(n_gram_sequence)\n\n# Find the maximum sequence length\nmax_sequence_len = max(len(seq) for seq in input_sequence)\n\n# Pad sequences to\npadded_sequences = pad_sequences(input_sequence, maxlen=max_sequence_len, padding='pre')\nprint(padded_sequences)\n\n# Summary:\n# This code initializes a Tokenizer, fits it on a list of sentences to build the vocabulary,\n# and then prints the total number of unique words and their corresponding indices in the word index.","metadata":{"_uuid":"243d4c53-c0b2-4114-92bb-28ae75f0c26d","_cell_guid":"15a1e922-2ce2-4356-9062-889b6ea92b43","collapsed":false,"id":"_D_f5mhN-20R","jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-08-09T08:27:21.103168Z","iopub.execute_input":"2024-08-09T08:27:21.103458Z","iopub.status.idle":"2024-08-09T08:27:21.174203Z","shell.execute_reply.started":"2024-08-09T08:27:21.103433Z","shell.execute_reply":"2024-08-09T08:27:21.173291Z"},"trusted":true},"execution_count":123,"outputs":[{"name":"stdout","text":"75\n{'learning': 1, 'data': 2, 'helps': 3, 'is': 4, 'for': 5, 'in': 6, 'using': 7, 'feature': 8, 'model': 9, 'language': 10, 'cross': 11, 'validation': 12, 'to': 13, 'evaluate': 14, 'supervised': 15, 'requires': 16, 'science': 17, 'involves': 18, 'and': 19, 'machine': 20, 'used': 21, 'engineering': 22, 'important': 23, 'deep': 24, 'models': 25, 'are': 26, 'trained': 27, 'unsupervised': 28, 'finds': 29, 'natural': 30, 'processing': 31, 'overfitting': 32, 'happens': 33, 'when': 34, 'the': 35, 'learns': 36, 'classification': 37, 'reinforcement': 38, 'trains': 39, 'agents': 40, 'gradient': 41, 'descent': 42, 'selection': 43, 'tuning': 44, 'labeled': 45, 'visualization': 46, 'feedback': 47, 'outliers': 48, 'clustering': 49, 'unlabeled': 50, 'regression': 51, 'structures': 52, 'prediction': 53, 'irrelevant': 54, 'patterns': 55, 'support': 56, 'vector': 57, 'machines': 58, 'decision': 59, 'trees': 60, 'neural': 61, 'networks': 62, 'cleaning': 63, 'stochastic': 64, 'text': 65, 'translation': 66, 'backpropagation': 67, 'clusters': 68, 'positive': 69, 'rewards': 70, 'negative': 71, 'sentiment': 72, 'analysis': 73, 'anomalies': 74}\n[[ 0  0  0 ...  0  0 15]\n [ 0  0  0 ...  0 15  1]\n [ 0  0  0 ... 15  1 16]\n ...\n [ 0  0  0 ...  3 13 14]\n [ 0  0  0 ... 13 14 61]\n [ 0  0 11 ... 14 61 62]]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#Split the data into features (x) and targets (y)","metadata":{"_uuid":"4423d631-48c1-4122-b293-791095454402","_cell_guid":"ff3725ca-85e8-4599-850d-381d203c4670","id":"mi3v4x6IMaLa","trusted":true}},{"cell_type":"code","source":"# Split the data into features (x) and targets (y)\n# Features (x) include all columns except the last one\nx_train = padded_sequences[:, :-1]\n\n# Targets (y) include only the last column\ny_train = padded_sequences[:, -1]\n\n# Print the features to verify their content\nprint(\"Features (x):\")\nprint(x_train)\n\n# Print the targets to verify their content\nprint(\"Targets (y):\")\nprint(y_train)","metadata":{"_uuid":"ab964a96-3d12-4a33-80eb-d24b99de8e84","_cell_guid":"eec8d41f-ed46-4f15-a9cc-107ad2f390c6","collapsed":false,"id":"awoKRkeOKljs","jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-08-09T08:27:21.175511Z","iopub.execute_input":"2024-08-09T08:27:21.175857Z","iopub.status.idle":"2024-08-09T08:27:21.182237Z","shell.execute_reply.started":"2024-08-09T08:27:21.175832Z","shell.execute_reply":"2024-08-09T08:27:21.181265Z"},"trusted":true},"execution_count":124,"outputs":[{"name":"stdout","text":"Features (x):\n[[ 0  0  0 ...  0  0  0]\n [ 0  0  0 ...  0  0 15]\n [ 0  0  0 ...  0 15  1]\n ...\n [ 0  0  0 ... 12  3 13]\n [ 0  0  0 ...  3 13 14]\n [ 0  0 11 ... 13 14 61]]\nTargets (y):\n[15  1 16 ... 14 61 62]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Convert the target data (y) to one-hot encoded format","metadata":{"_uuid":"e668bf12-707d-4c05-b782-666749c7a6e0","_cell_guid":"172b302a-ff1d-4f9b-a90c-fdd066e62868","id":"rtK07Ie-MT8L","trusted":true}},{"cell_type":"code","source":"\n# Convert the target data (y) to one-hot encoded format\n# 'num_classes' specifies the total number of classes (e.g., total_words)\ny_train = tf.keras.utils.to_categorical(y_train, num_classes=total_words)\n\n# Print the one-hot encoded target data to verify the result\nprint(\"One-hot encoded targets (y):\")\nprint(y_train)","metadata":{"_uuid":"5563131b-e5f3-4d14-9eeb-f12feb075283","_cell_guid":"5d6db3b6-fac5-44ea-8f90-5704e6c50634","collapsed":false,"id":"NzESdygFKwiD","jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-08-09T08:27:21.183316Z","iopub.execute_input":"2024-08-09T08:27:21.183617Z","iopub.status.idle":"2024-08-09T08:27:21.192768Z","shell.execute_reply.started":"2024-08-09T08:27:21.183592Z","shell.execute_reply":"2024-08-09T08:27:21.191817Z"},"trusted":true},"execution_count":125,"outputs":[{"name":"stdout","text":"One-hot encoded targets (y):\n[[0. 0. 0. ... 0. 0. 0.]\n [0. 1. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Define Architecture LSTM Long Short-Term Memory","metadata":{"_uuid":"e69abe57-2fcf-42b3-92b1-2158b8fac072","_cell_guid":"f6c96481-72d7-4a07-be75-00616919d52c","id":"VFln_XRhMKe1","trusted":true}},{"cell_type":"code","source":"import numpy as np\n\n# Assuming y_train is 2D, reshape it to 1D if needed\ny_train = np.squeeze(y_train)\n\n# Now check the shape\nprint(y_train.shape)","metadata":{"_uuid":"61fa03ca-1269-493e-a06d-1fe6cc949beb","_cell_guid":"195c6ea1-f86c-46ed-bbce-4a4dd1e91cda","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-08-09T08:27:21.197985Z","iopub.execute_input":"2024-08-09T08:27:21.198265Z","iopub.status.idle":"2024-08-09T08:27:21.203877Z","shell.execute_reply.started":"2024-08-09T08:27:21.198241Z","shell.execute_reply":"2024-08-09T08:27:21.202993Z"},"trusted":true},"execution_count":126,"outputs":[{"name":"stdout","text":"(6750, 75)\n","output_type":"stream"}]},{"cell_type":"code","source":"model = Sequential([\n    # Embedding layer: converts input sequences of integers into dense vectors of fixed size\n    Embedding(total_words, 10,           # Dimension of the dense embedding vectors\n              input_length=max_sequence_len-1),  # Length of input sequences\n\n    # Simple Lstm layer: processes the sequence data and retains the temporal information\n    LSTM(100),  # Number of units in the lstm layer\n\n    # Dense layer: outputs the probability distribution over the vocabulary\n    Dense(total_words,            # Size of the output layer (same as vocabulary size)\n          activation='softmax')   # Softmax activation to produce probability distribution\n])","metadata":{"_uuid":"73b3bd95-44a2-4b26-8afd-e4e337c81918","_cell_guid":"d3257a80-5814-461f-a4fa-9b00e806d3b2","collapsed":false,"id":"7q5Epb1pLlB5","jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-08-09T08:27:21.205140Z","iopub.execute_input":"2024-08-09T08:27:21.205439Z","iopub.status.idle":"2024-08-09T08:27:21.222356Z","shell.execute_reply.started":"2024-08-09T08:27:21.205414Z","shell.execute_reply":"2024-08-09T08:27:21.221454Z"},"trusted":true},"execution_count":127,"outputs":[]},{"cell_type":"code","source":"model.compile(\n    optimizer='adam',\n    loss='categorical_crossentropy',  # Use this for multi-class classification\n    metrics=['accuracy']\n)","metadata":{"_uuid":"26b6e640-bf06-4cdb-a1a1-be518d0675cf","_cell_guid":"ebc411d2-746b-4ae9-92bb-733beac8d47b","collapsed":false,"id":"tMpihk36M8Uc","jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-08-09T08:27:21.223658Z","iopub.execute_input":"2024-08-09T08:27:21.224085Z","iopub.status.idle":"2024-08-09T08:27:21.232274Z","shell.execute_reply.started":"2024-08-09T08:27:21.224053Z","shell.execute_reply":"2024-08-09T08:27:21.231495Z"},"trusted":true},"execution_count":128,"outputs":[]},{"cell_type":"code","source":"# Train the model with a specified batch size\nmodel.fit(\n    x_train,\n    y_train,\n    epochs=50,\n    batch_size=32,  # Specify the batch size here\n    verbose=1  # Progress bar\n)","metadata":{"_uuid":"cf14b978-8b19-43d5-ae28-6e5a176e360d","_cell_guid":"9f4270f8-daa2-40d7-bcf8-4c9f7fd5621f","collapsed":false,"id":"6DAZJH3xNLZ6","jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-08-09T08:27:21.233436Z","iopub.execute_input":"2024-08-09T08:27:21.233972Z","iopub.status.idle":"2024-08-09T08:28:04.580151Z","shell.execute_reply.started":"2024-08-09T08:27:21.233941Z","shell.execute_reply":"2024-08-09T08:28:04.579218Z"},"trusted":true},"execution_count":129,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.0990 - loss: 3.9826\nEpoch 2/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.3416 - loss: 2.4759\nEpoch 3/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.5838 - loss: 1.4378\nEpoch 4/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6762 - loss: 0.9895\nEpoch 5/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7467 - loss: 0.7701\nEpoch 6/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7556 - loss: 0.6730\nEpoch 7/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7673 - loss: 0.6006\nEpoch 8/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7770 - loss: 0.5673\nEpoch 9/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7783 - loss: 0.5362\nEpoch 10/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7602 - loss: 0.5615\nEpoch 11/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7685 - loss: 0.5517\nEpoch 12/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7808 - loss: 0.5168\nEpoch 13/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7659 - loss: 0.5481\nEpoch 14/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7704 - loss: 0.5210\nEpoch 15/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7754 - loss: 0.5147\nEpoch 16/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7723 - loss: 0.5016\nEpoch 17/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7691 - loss: 0.5178\nEpoch 18/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7650 - loss: 0.5204\nEpoch 19/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7720 - loss: 0.5149\nEpoch 20/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7708 - loss: 0.5128\nEpoch 21/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7688 - loss: 0.5171\nEpoch 22/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7612 - loss: 0.5296\nEpoch 23/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7769 - loss: 0.5113\nEpoch 24/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7621 - loss: 0.5233\nEpoch 25/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7685 - loss: 0.5147\nEpoch 26/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7580 - loss: 0.5291\nEpoch 27/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7628 - loss: 0.5163\nEpoch 28/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7665 - loss: 0.5172\nEpoch 29/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7675 - loss: 0.5172\nEpoch 30/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7661 - loss: 0.5033\nEpoch 31/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7705 - loss: 0.5031\nEpoch 32/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7698 - loss: 0.5144\nEpoch 33/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7719 - loss: 0.5166\nEpoch 34/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7662 - loss: 0.5149\nEpoch 35/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7724 - loss: 0.5095\nEpoch 36/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7793 - loss: 0.5004\nEpoch 37/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7600 - loss: 0.5282\nEpoch 38/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7687 - loss: 0.5147\nEpoch 39/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7780 - loss: 0.5048\nEpoch 40/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7666 - loss: 0.5257\nEpoch 41/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7727 - loss: 0.5019\nEpoch 42/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7668 - loss: 0.5185\nEpoch 43/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7637 - loss: 0.5237\nEpoch 44/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7678 - loss: 0.5236\nEpoch 45/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7705 - loss: 0.5185\nEpoch 46/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7726 - loss: 0.5092\nEpoch 47/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7697 - loss: 0.5158\nEpoch 48/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7694 - loss: 0.5108\nEpoch 49/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7686 - loss: 0.5094\nEpoch 50/50\n\u001b[1m211/211\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7623 - loss: 0.5245\n","output_type":"stream"},{"execution_count":129,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x7e9a9eff9210>"},"metadata":{}}]},{"cell_type":"code","source":"\n# This function predicts the next word(s) given a starting piece of text (seed_text).\ndef predict_next_word(seed_text, next_words=1):\n    # Loop to predict the next word 'next_words' times\n    for _ in range(next_words):\n        # Convert the seed text into a format that the model can understand (a sequence of numbers)\n        token_list = tokenizer.texts_to_sequences([seed_text])\n\n        # Ensure the sequence is the right length for the model by padding it with zeros if needed\n        token_list = pad_sequences(token_list, maxlen=max_sequence_len, padding='pre')\n\n        # Use the model to predict the probabilities of the next word\n        predicted_probs = model.predict(token_list, verbose=0)\n        # Find the index of the word with the highest probability\n        predicted_index = np.argmax(predicted_probs, axis=-1)[0]\n\n        # Convert the index back into the actual word\n        predicted_word = tokenizer.index_word.get(predicted_index, '')\n\n        # Add the predicted word to the end of the seed text\n        seed_text += ' ' + predicted_word\n\n    # Return the updated seed text with the predicted words added\n    return seed_text\n\n# Example usage of the function:\nseed_text = \"machine learning is used for?\"\n# Predict the next 4 words based on the seed text\npredicted_text = predict_next_word(seed_text, next_words=10)\n# Print the result\nprint(predicted_text)","metadata":{"_uuid":"14556f56-61d5-4e09-9057-4ab88c69629f","_cell_guid":"28c718b2-e354-435b-bd95-a0c6d9814aaa","collapsed":false,"id":"Y9SUT2woOAgj","jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]}]}