gender-prediction-based-on-name-using-lstm.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\nimport tensorflow\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.models import load_model\n\nimport sklearn\nfrom sklearn.model_selection import train_test_split","metadata":{"execution":{"iopub.status.busy":"2023-01-16T18:23:48.634615Z","iopub.execute_input":"2023-01-16T18:23:48.635012Z","iopub.status.idle":"2023-01-16T18:23:48.642151Z","shell.execute_reply.started":"2023-01-16T18:23:48.634981Z","shell.execute_reply":"2023-01-16T18:23:48.641008Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"names_df = pd.read_csv(\"/kaggle/input/gender-based-names/name_gender.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-01-16T17:13:10.740493Z","iopub.execute_input":"2023-01-16T17:13:10.741446Z","iopub.status.idle":"2023-01-16T17:13:10.848709Z","shell.execute_reply.started":"2023-01-16T17:13:10.741392Z","shell.execute_reply":"2023-01-16T17:13:10.847620Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"names_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-01-16T17:13:16.633340Z","iopub.execute_input":"2023-01-16T17:13:16.633796Z","iopub.status.idle":"2023-01-16T17:13:16.674093Z","shell.execute_reply.started":"2023-01-16T17:13:16.633751Z","shell.execute_reply":"2023-01-16T17:13:16.672826Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 95025 entries, 0 to 95024\nData columns (total 3 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   Name        95025 non-null  object \n 1   Gender      95025 non-null  object \n 2   confidence  95025 non-null  float64\ndtypes: float64(1), object(2)\nmemory usage: 2.2+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"def preprocess(names_df, train=True):\n    # Step 1: Lowercase\n    names_df['Name'] = names_df['Name'].str.lower()\n\n    # Step 2: Split individual characters\n    names_df['Name'] = [list(name) for name in names_df['Name']]\n\n    # Step 3: Pad names with spaces to make all names same length\n    name_length = 50\n    names_df['Name'] = [\n        (name + [' ']*name_length)[:name_length] \n        for name in names_df['Name']\n    ]\n\n    # Step 4: Encode Characters to Numbers\n    names_df['Name'] = [\n        [\n            max(0.0, ord(char)-96.0) \n            for char in name\n        ]\n        for name in names_df['Name']\n    ]\n    \n    if train:\n        # Step 5: Encode Gender to Numbers\n        names_df['Gender'] = [\n            0.0 if gender=='F' else 1.0 \n            for gender in names_df['Gender']\n        ]\n    \n    return names_df\n\nnames_df = preprocess(names_df)\nnames_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-16T17:13:24.034905Z","iopub.execute_input":"2023-01-16T17:13:24.035451Z","iopub.status.idle":"2023-01-16T17:13:26.684106Z","shell.execute_reply.started":"2023-01-16T17:13:24.035399Z","shell.execute_reply":"2023-01-16T17:13:26.682623Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"                                                Name  Gender  confidence\n0  [1.0, 1.0, 2.0, 1.0, 14.0, 0.0, 0.0, 0.0, 0.0,...     1.0         1.0\n1  [1.0, 1.0, 2.0, 8.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...     0.0         1.0\n2  [1.0, 1.0, 2.0, 9.0, 4.0, 0.0, 0.0, 0.0, 0.0, ...     1.0         1.0\n3  [1.0, 1.0, 2.0, 18.0, 9.0, 5.0, 12.0, 12.0, 1....     0.0         1.0\n4  [1.0, 1.0, 4.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...     0.0         1.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>Gender</th>\n      <th>confidence</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>[1.0, 1.0, 2.0, 1.0, 14.0, 0.0, 0.0, 0.0, 0.0,...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>[1.0, 1.0, 2.0, 8.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>[1.0, 1.0, 2.0, 9.0, 4.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>[1.0, 1.0, 2.0, 18.0, 9.0, 5.0, 12.0, 12.0, 1....</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>[1.0, 1.0, 4.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"def lstm_model(num_alphabets=27, name_length=50, embedding_dim=256):\n    model = Sequential([\n        Embedding(num_alphabets, embedding_dim, input_length=name_length),\n        Bidirectional(LSTM(units=128, recurrent_dropout=0.2, dropout=0.2)),\n        Dense(1, activation=\"sigmoid\")\n    ])\n\n    model.compile(loss='binary_crossentropy',\n                  optimizer=Adam(learning_rate=0.001),\n                  metrics=['accuracy'])\n\n    return model","metadata":{"execution":{"iopub.status.busy":"2023-01-16T17:13:47.440500Z","iopub.execute_input":"2023-01-16T17:13:47.440921Z","iopub.status.idle":"2023-01-16T17:13:47.448273Z","shell.execute_reply.started":"2023-01-16T17:13:47.440888Z","shell.execute_reply":"2023-01-16T17:13:47.446914Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"# Step 1: Instantiate the model\nmodel = lstm_model(num_alphabets=27, name_length=50, embedding_dim=256)","metadata":{"execution":{"iopub.status.busy":"2023-01-16T17:13:52.010000Z","iopub.execute_input":"2023-01-16T17:13:52.010440Z","iopub.status.idle":"2023-01-16T17:13:52.436012Z","shell.execute_reply.started":"2023-01-16T17:13:52.010403Z","shell.execute_reply":"2023-01-16T17:13:52.435138Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stderr","text":"2023-01-16 17:13:52.058226: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Step 2: Split Training and Test Data\nX = np.asarray(names_df['Name'].values.tolist())\ny = np.asarray(names_df['Gender'].values.tolist())\n\nX_train, X_test, y_train, y_test = train_test_split(X,\n                                                    y,\n                                                    test_size=0.2,\n                                                    random_state=0)\n","metadata":{"execution":{"iopub.status.busy":"2023-01-16T17:13:57.441496Z","iopub.execute_input":"2023-01-16T17:13:57.441968Z","iopub.status.idle":"2023-01-16T17:13:57.866118Z","shell.execute_reply.started":"2023-01-16T17:13:57.441930Z","shell.execute_reply":"2023-01-16T17:13:57.865204Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"# Step 3: Train the model\ncallbacks = [\n    EarlyStopping(monitor='val_accuracy',\n                  min_delta=1e-3,\n                  patience=5,\n                  mode='max',\n                  restore_best_weights=True,\n                  verbose=1),\n]\n\nhistory = model.fit(x=X_train,\n                    y=y_train,\n                    batch_size=64,\n                    epochs=5,\n                    validation_data=(X_test, y_test),\n                    callbacks=callbacks)\n","metadata":{"execution":{"iopub.status.busy":"2023-01-16T17:14:10.703808Z","iopub.execute_input":"2023-01-16T17:14:10.704212Z","iopub.status.idle":"2023-01-16T18:17:29.755904Z","shell.execute_reply.started":"2023-01-16T17:14:10.704177Z","shell.execute_reply":"2023-01-16T18:17:29.754964Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"2023-01-16 17:14:10.787946: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/5\n1188/1188 [==============================] - 773s 644ms/step - loss: 0.4144 - accuracy: 0.8107 - val_loss: 0.3674 - val_accuracy: 0.8395\nEpoch 2/5\n1188/1188 [==============================] - 762s 641ms/step - loss: 0.3481 - accuracy: 0.8482 - val_loss: 0.3310 - val_accuracy: 0.8574\nEpoch 3/5\n1188/1188 [==============================] - 757s 637ms/step - loss: 0.3194 - accuracy: 0.8625 - val_loss: 0.2937 - val_accuracy: 0.8745\nEpoch 4/5\n1188/1188 [==============================] - 761s 641ms/step - loss: 0.2927 - accuracy: 0.8760 - val_loss: 0.2732 - val_accuracy: 0.8851\nEpoch 5/5\n1188/1188 [==============================] - 746s 628ms/step - loss: 0.2701 - accuracy: 0.8865 - val_loss: 0.2576 - val_accuracy: 0.8929\n","output_type":"stream"}]},{"cell_type":"code","source":"# Step 4: Save the model\nmodel.save('boyorgirl_5.h5')","metadata":{"execution":{"iopub.status.busy":"2023-01-16T18:18:00.831422Z","iopub.execute_input":"2023-01-16T18:18:00.831852Z","iopub.status.idle":"2023-01-16T18:18:00.870538Z","shell.execute_reply.started":"2023-01-16T18:18:00.831817Z","shell.execute_reply":"2023-01-16T18:18:00.869463Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# Step 5: Plot accuracies\nplt.plot(history.history['accuracy'], label='train')\nplt.plot(history.history['val_accuracy'], label='val')\nplt.xlabel('Epochs')\nplt.ylabel('Accuracy')\nplt.legend()","metadata":{"execution":{"iopub.status.busy":"2023-01-16T18:18:16.325534Z","iopub.execute_input":"2023-01-16T18:18:16.325946Z","iopub.status.idle":"2023-01-16T18:18:16.552918Z","shell.execute_reply.started":"2023-01-16T18:18:16.325914Z","shell.execute_reply":"2023-01-16T18:18:16.551807Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"<matplotlib.legend.Legend at 0x7f0d19b99e50>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Testing: Predictions","metadata":{}},{"cell_type":"code","source":"#Load the model\npred_model = load_model('boyorgirl_5.h5')","metadata":{"execution":{"iopub.status.busy":"2023-01-16T18:18:32.290091Z","iopub.execute_input":"2023-01-16T18:18:32.291243Z","iopub.status.idle":"2023-01-16T18:18:32.584689Z","shell.execute_reply.started":"2023-01-16T18:18:32.291205Z","shell.execute_reply":"2023-01-16T18:18:32.583762Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"# Input names\nnames = ['akshaya']\n\n# Convert to dataframe\npred_df = pd.DataFrame({'Name': names})\n\n# Preprocess\npred_df = preprocess(pred_df, train=False)\n\n# Predictions\nresult = pred_model.predict(np.asarray(\n    pred_df['Name'].values.tolist())).squeeze(axis=1)\n\npred_df['Boy or Girl?'] = [\n    'Boy' if logit > 0.5 else 'Girl' for logit in result\n]\n\npred_df['Probability'] = [\n    logit if logit > 0.5 else 1.0 - logit for logit in result\n]\n\n# Format the output\npred_df['Name'] = names\n#pred_df.rename(columns={'Name': 'Name'}, inplace=True)\npred_df['Probability'] = pred_df['Probability'].round(2)\npred_df.drop_duplicates(inplace=True)\n\npred_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-16T18:21:51.822342Z","iopub.execute_input":"2023-01-16T18:21:51.822788Z","iopub.status.idle":"2023-01-16T18:21:51.975889Z","shell.execute_reply.started":"2023-01-16T18:21:51.822734Z","shell.execute_reply":"2023-01-16T18:21:51.975061Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"      Name Boy or Girl?  Probability\n0  akshaya         Girl          0.7","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>Boy or Girl?</th>\n      <th>Probability</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>akshaya</td>\n      <td>Girl</td>\n      <td>0.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**If you like my work, Please Upvote**\n**Thank you**","metadata":{}}]}