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      "body": "Congratulations @ultimate.duwal! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@ultimate.duwal/birthday3.png</td><td>Happy Birthday! - You are on the Steem blockchain for 3 years!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@ultimate.duwal) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=ultimate.duwal)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
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      "body": "In most cases, for training the model with the dataset we have is very time consuming and also processing hungry job which is costly task. To test in our development environment we have to do is test the trained model or use the trained model for in production without going for multiple training.\n\nIf you have done some ML project you would have understood, how time and processor consuming task it is even when done in GPUs. For a application to use the model and train each time the application runs is unacceptable, so we can save the current trained state of the model for later use without of retraining the model on the same dataset again and again.\n\nWe can accomplish this in python using some packages like\n\nPickle (Python Object Serialization Library)\nJoblib (One of the Scikit-learn Method)\nPickle?\n\nYou might have heard this term somewhere when you go though ML articles or doing projects. This library is popular for Serialization(Pickling) and Marshalling (Unpickling). Pickling is the process of converting any Python object into a stream of bytes in hierarchy.Unpickling is  a process of converting the pickled stream of bytes to original python object following the object hierarchy.\n\nExample:\n\nSerialization (Pickling)\nimport pickle\n\npickle_file = 'string_list_pickle.pkl'\nnames = ['apple', 'ball', 'cat']\n\nstore_pickle = open(pickle_file, 'wb')\npickle.dump(names, store_pickle)\nstore_pickle.close()\nMarshalling (Unpickling)\nimport pickle\npickle_file = 'string_list_pickle.pkl'\nunpickling_list = open(pickle_file, 'r')\n\nnames_list = pickle.load(unpickling_list)\nprint (\"Name in pickled list: \", names_list)\nOk then this is simple usage of how picking is done with Pickle.\nWe will now work with a ML model for classification. A Decision Tree classifier is good point to start.\n\nimport pickle\nimport pandas\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\n\n# load dataset\n\nload_balance_scale_dataset = pandas.read_csv(\n    \"//archive.ics.uci.edu/ml/machine-learning-databases/balance-scale/balance-scale.data\", sep=',', header=None)\n\nprint \"Dataset length: \", len(load_balance_scale_dataset)\nprint \"Dataset Shape: \", load_balance_scale_dataset.shape\n\nX = load_balance_scale_dataset.values[:, 1:5]\nY = load_balance_scale_dataset.values[:, 0]\n\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=100)\n\ndecision_tree_model = DecisionTreeClassifier(criterion=\"gini\", random_state=100, max_depth=3, min_samples_leaf=5)\n\ndecision_tree_model.fit(X_train, y_train)\nprint (\"Decision tree classifier: \", decision_tree_model)\n\n# dumping the model\ndecision_tree_pkl = 'decision_tree_classifier.pkl'\ndecision_tree_model_pkl = open(decision_tree_pkl, 'wb')\n\npickle.dump(decision_tree_model, decision_tree_model_pkl)\ndecision_tree_model_pkl.close()\n\n# loading the model\ndecision_tree_model_pkl = open(decision_tree_pkl, 'rb')\ndecision_tree_model = pickle.load(decision_tree_model_pkl)\nprint (\"Loaded model: \", decision_tree_model)\nIts late night already and sleepy long before. But couldn’t help myself to write down this pickle from writing in this blog.\n\nI will continue writing to classify balanced scale model using picked dataset also, classifier for TIC TAC TOE dataset (if you wondering what that load_tic_tac_toe_dataset variable meant) to classify if a board state is winning state for x or losing state for x.\n\nAlso, I haven’t forgotten about Joblib, oh no I haven’t. Wait for next post. 😉 ;P\nGood night guys",
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