The goal of the PowerLift Muscle Map project is to build and evaluate predictive models to estimate the best deadlift weight based on the available features. We will explore different models, analyze feature importance, and visualize the impact of various factors on lifting performance.\n",
+ "\n",
+ " It is a great project also help individual in gym to predicted their Maximum kg Deadlift based on some information give motivation to them to achieve their predicted goal.\n",
+ "\n",
+ " **!!Caution**:\n",
+ " - For Beginner :\n",
+ " - When starting with deadlift training, it's important to begin with half of your predicted maximum weight. Gradually increase the weight in small steps, focusing on improving your form and boosting your confidence with each lift. This approach helps you build strength safely and effectively.\n",
+ "\n",
+ "\n",
+ "\n",
+ " *Precaution is better than injury.*\n",
+ " Rule for Gym || Rule for Life\n",
+ " ....Time to Level up guys....\n",
+ " (2 AM thoughts-PDS)\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z17AV9jF4DwK"
+ },
+ "source": [
+ "### Importing useful libraries\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "L9hevfYznJ1v"
+ },
+ "outputs": [],
+ "source": [
+ "#importing all libraries\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "R2-waKmjnJ1x"
+ },
+ "outputs": [],
+ "source": [
+ "#imoprting sklearn libraries\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "from sklearn.metrics import r2_score\n",
+ "from sklearn.preprocessing import OneHotEncoder\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "-LwDC6s0-0Fp"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.compose import ColumnTransformer\n",
+ "from sklearn.pipeline import Pipeline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "m8EtVVD9oTYr"
+ },
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Pbm65MtE4NN7"
+ },
+ "source": [
+ "### Creating dataframe by reading dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "nwSKjywtnJ1y"
+ },
+ "outputs": [],
+ "source": [
+ "#read datasets\n",
+ "df=pd.read_csv('gym_powerlifting_correct.csv')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "qZ5lYRhknJ1y",
+ "outputId": "ac477fbf-4a56-479a-a5a7-f00ebd2424cf"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | playerId | \n", + "Name | \n", + "Sex | \n", + "Equipment | \n", + "Age | \n", + "BodyweightKg | \n", + "BestSquatKg | \n", + "BestDeadliftKg | \n", + "Bestbench(kg) | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "Russell Williams | \n", + "M | \n", + "Raw | \n", + "76.0 | \n", + "83.35 | \n", + "107.5 | \n", + "132.5 | \n", + "62.5 | \n", + "
| 1 | \n", + "1 | \n", + "Daniel Johnson | \n", + "M | \n", + "Raw | \n", + "25.0 | \n", + "88.81 | \n", + "215.0 | \n", + "255.0 | \n", + "137.5 | \n", + "
| 2 | \n", + "2 | \n", + "Joshua Chambers | \n", + "M | \n", + "Raw | \n", + "20.5 | \n", + "117.95 | \n", + "207.5 | \n", + "210.0 | \n", + "110.0 | \n", + "
| 3 | \n", + "3 | \n", + "Darren Sun | \n", + "M | \n", + "Raw | \n", + "21.0 | \n", + "73.98 | \n", + "192.5 | \n", + "222.5 | \n", + "115.0 | \n", + "
| 4 | \n", + "4 | \n", + "Madelyn Day | \n", + "F | \n", + "Raw | \n", + "17.5 | \n", + "46.00 | \n", + "62.5 | \n", + "105.0 | \n", + "42.5 | \n", + "
| \n", + " | Sex | \n", + "Equipment | \n", + "Age | \n", + "BodyweightKg | \n", + "BestSquatKg | \n", + "BestDeadliftKg | \n", + "Bestbench(kg) | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "M | \n", + "Raw | \n", + "76.0 | \n", + "83.35 | \n", + "107.5 | \n", + "132.5 | \n", + "62.5 | \n", + "
| 1 | \n", + "M | \n", + "Raw | \n", + "25.0 | \n", + "88.81 | \n", + "215.0 | \n", + "255.0 | \n", + "137.5 | \n", + "
| 2 | \n", + "M | \n", + "Raw | \n", + "20.5 | \n", + "117.95 | \n", + "207.5 | \n", + "210.0 | \n", + "110.0 | \n", + "
| 3 | \n", + "M | \n", + "Raw | \n", + "21.0 | \n", + "73.98 | \n", + "192.5 | \n", + "222.5 | \n", + "115.0 | \n", + "
| 4 | \n", + "F | \n", + "Raw | \n", + "17.5 | \n", + "46.00 | \n", + "62.5 | \n", + "105.0 | \n", + "42.5 | \n", + "
| \n", + " | Age | \n", + "BodyweightKg | \n", + "BestSquatKg | \n", + "BestDeadliftKg | \n", + "Bestbench(kg) | \n", + "
|---|---|---|---|---|---|
| count | \n", + "29580.000000 | \n", + "29580.000000 | \n", + "29580.000000 | \n", + "29580.000000 | \n", + "29580.000000 | \n", + "
| mean | \n", + "29.678786 | \n", + "85.346240 | \n", + "178.229907 | \n", + "201.086612 | \n", + "116.456714 | \n", + "
| std | \n", + "11.576102 | \n", + "22.881544 | \n", + "71.793053 | \n", + "62.412353 | \n", + "52.365008 | \n", + "
| min | \n", + "7.000000 | \n", + "24.100000 | \n", + "-330.000000 | \n", + "18.100000 | \n", + "-280.000000 | \n", + "
| 25% | \n", + "21.500000 | \n", + "68.000000 | \n", + "122.500000 | \n", + "147.500000 | \n", + "72.500000 | \n", + "
| 50% | \n", + "26.500000 | \n", + "82.095000 | \n", + "175.000000 | \n", + "204.120000 | \n", + "115.000000 | \n", + "
| 75% | \n", + "35.000000 | \n", + "98.880000 | \n", + "222.500000 | \n", + "247.500000 | \n", + "150.000000 | \n", + "
| max | \n", + "89.500000 | \n", + "242.400000 | \n", + "532.970000 | \n", + "420.500000 | \n", + "425.000000 | \n", + "
| \n", + " | Sex | \n", + "Equipment | \n", + "Age | \n", + "BodyweightKg | \n", + "BestSquatKg | \n", + "BestDeadliftKg | \n", + "Bestbench(kg) | \n", + "
|---|---|---|---|---|---|---|---|
| 1 | \n", + "M | \n", + "Raw | \n", + "25.0 | \n", + "88.81 | \n", + "215.0 | \n", + "255.0 | \n", + "137.5 | \n", + "
| \n", + " | Age | \n", + "BodyweightKg | \n", + "BestSquatKg | \n", + "BestDeadliftKg | \n", + "Bestbench(kg) | \n", + "
|---|---|---|---|---|---|
| count | \n", + "27406.000000 | \n", + "27406.000000 | \n", + "27406.000000 | \n", + "27406.000000 | \n", + "27406.000000 | \n", + "
| mean | \n", + "28.102605 | \n", + "83.786106 | \n", + "177.039435 | \n", + "200.129554 | \n", + "114.937492 | \n", + "
| std | \n", + "9.291712 | \n", + "20.769594 | \n", + "64.213182 | \n", + "60.774771 | \n", + "47.770977 | \n", + "
| min | \n", + "7.000000 | \n", + "24.100000 | \n", + "13.600000 | \n", + "18.100000 | \n", + "9.100000 | \n", + "
| 25% | \n", + "21.500000 | \n", + "67.400000 | \n", + "125.000000 | \n", + "147.500000 | \n", + "72.500000 | \n", + "
| 50% | \n", + "26.000000 | \n", + "81.700000 | \n", + "175.000000 | \n", + "202.500000 | \n", + "115.000000 | \n", + "
| 75% | \n", + "33.000000 | \n", + "98.000000 | \n", + "220.000000 | \n", + "245.000000 | \n", + "147.500000 | \n", + "
| max | \n", + "55.000000 | \n", + "145.700000 | \n", + "367.500000 | \n", + "387.500000 | \n", + "265.000000 | \n", + "
| \n", + " | Sex | \n", + "Equipment | \n", + "Age | \n", + "BodyweightKg | \n", + "BestSquatKg | \n", + "Bestbench(kg) | \n", + "
|---|---|---|---|---|---|---|
| 1 | \n", + "M | \n", + "Raw | \n", + "25.0 | \n", + "88.81 | \n", + "215.0 | \n", + "137.5 | \n", + "
| 2 | \n", + "M | \n", + "Raw | \n", + "20.5 | \n", + "117.95 | \n", + "207.5 | \n", + "110.0 | \n", + "
| 3 | \n", + "M | \n", + "Raw | \n", + "21.0 | \n", + "73.98 | \n", + "192.5 | \n", + "115.0 | \n", + "
| 4 | \n", + "F | \n", + "Raw | \n", + "17.5 | \n", + "46.00 | \n", + "62.5 | \n", + "42.5 | \n", + "
| 5 | \n", + "F | \n", + "Raw | \n", + "28.0 | \n", + "48.00 | \n", + "92.5 | \n", + "47.5 | \n", + "
ColumnTransformer(transformers=[('cat',\n",
+ " Pipeline(steps=[('onehot',\n",
+ " OneHotEncoder(handle_unknown='ignore'))]),\n",
+ " ['Sex', 'Equipment']),\n",
+ " ('num',\n",
+ " Pipeline(steps=[('scaler', StandardScaler())]),\n",
+ " ['Age', 'BodyweightKg', 'BestSquatKg',\n",
+ " 'Bestbench(kg)'])])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. ColumnTransformer(transformers=[('cat',\n",
+ " Pipeline(steps=[('onehot',\n",
+ " OneHotEncoder(handle_unknown='ignore'))]),\n",
+ " ['Sex', 'Equipment']),\n",
+ " ('num',\n",
+ " Pipeline(steps=[('scaler', StandardScaler())]),\n",
+ " ['Age', 'BodyweightKg', 'BestSquatKg',\n",
+ " 'Bestbench(kg)'])])['Sex', 'Equipment']
OneHotEncoder(handle_unknown='ignore')
['Age', 'BodyweightKg', 'BestSquatKg', 'Bestbench(kg)']
StandardScaler()
| \n", + " | Age | \n", + "BodyweightKg | \n", + "BestSquatKg | \n", + "Bestbench(kg) | \n", + "Sex_F | \n", + "Sex_M | \n", + "Equipment_Multi-ply | \n", + "Equipment_Raw | \n", + "Equipment_Single-ply | \n", + "Equipment_Wraps | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "-0.333917 | \n", + "0.241891 | \n", + "0.591175 | \n", + "0.472314 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "0.0 | \n", + "
| 1 | \n", + "-0.818229 | \n", + "1.644930 | \n", + "0.474375 | \n", + "-0.103359 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "0.0 | \n", + "
| 2 | \n", + "-0.764416 | \n", + "-0.472146 | \n", + "0.240774 | \n", + "0.001309 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "0.0 | \n", + "
| 3 | \n", + "-1.141103 | \n", + "-1.819332 | \n", + "-1.783770 | \n", + "-1.516377 | \n", + "1.0 | \n", + "0.0 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "0.0 | \n", + "
| 4 | \n", + "-0.011043 | \n", + "-1.723036 | \n", + "-1.316567 | \n", + "-1.411709 | \n", + "1.0 | \n", + "0.0 | \n", + "0.0 | \n", + "1.0 | \n", + "0.0 | \n", + "0.0 | \n", + "
RandomForestRegressor(n_estimators=200, random_state=40)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor(n_estimators=200, random_state=40)
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
GradientBoostingRegressor(n_estimators=200, random_state=40)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GradientBoostingRegressor(n_estimators=200, random_state=40)
XGBRegressor(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=150, n_jobs=None,\n", + " num_parallel_tree=None, random_state=40, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
XGBRegressor(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=150, n_jobs=None,\n", + " num_parallel_tree=None, random_state=40, ...)
AdaBoostRegressor(n_estimators=150, random_state=40)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
AdaBoostRegressor(n_estimators=150, random_state=40)
LGBMRegressor(n_estimators=200, random_state=40)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LGBMRegressor(n_estimators=200, random_state=40)
KNeighborsRegressor(n_neighbors=10)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
KNeighborsRegressor(n_neighbors=10)