calories.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calories prediction from macronutrients and sodium\n",
    "\n",
    "This is a school project from the *\"Maths and Data Science for Decision Making\"* course. It is a simple model that aims to predict the number of calories (kcal) that a person consumes based on his or her carbohydrate (g), fats (g), protein (g) and sodium (mg) consumption.\n",
    "\n",
    "The data is mine and I have used it for the project, is the consume of calories, macros and sodium for a period of 12 weeks.\n",
    "\n",
    "The model is a multiple linear regression model with the variables:\n",
    "- **carbohydrate**: the number of grams of carbohydrate consumed per product\n",
    "- **fats**: the number of grams of fats consumed per product\n",
    "- **protein**: the number of grams of protein consumed per product\n",
    "- **sodium**: the number of milligrams of sodium consumed per product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All the libraries are loaded\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 420 rows in the database\n",
      "                         Nombre  Calories (kcal)  Carbohydrates (g)  Fats (g)  \\\n",
      "0                Licuado tipico           241.10              39.30      7.00   \n",
      "1                      Vive 100           125.00              28.00      0.00   \n",
      "2                   1/2 mollete           237.50              27.50     10.00   \n",
      "3                        Gringa           520.80              30.00     34.00   \n",
      "4  Comida (frij, bist, quesadi)           718.09              64.85     32.15   \n",
      "\n",
      "   Proteins (g)  Sodium (mg)  \n",
      "0          7.15       160.00  \n",
      "1          0.00       150.00  \n",
      "2         10.00       436.25  \n",
      "3         24.80       509.20  \n",
      "4         37.24       489.64  \n"
     ]
    }
   ],
   "source": [
    "# Load the database\n",
    "path = \"./database_cal.csv\"\n",
    "df = pd.read_csv(path)\n",
    "print(f\"There are {len(df)} rows in the database\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calories (kcal)      0.0\n",
      "Carbohydrates (g)    0.0\n",
      "Fats (g)             0.0\n",
      "Proteins (g)         0.0\n",
      "Sodium (mg)          0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Mantain the dataframe with only the columns we need\n",
    "df = df.select_dtypes(include=np.number)\n",
    "# Verify the percentage of NaN values\n",
    "print(df.isna().sum() / len(df)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Represent the consumption of calories\n",
    "sns.histplot(df[\"Calories (kcal)\"], bins=5, kde=False, color=\"green\")\n",
    "plt.title(\"Distribution of the calories\")\n",
    "plt.xlabel(\"Calories (kcal)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Represent the consumption of all variables\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "fig.suptitle(\"Distribution of the variables\", fontsize=20)\n",
    "\n",
    "sns.histplot(df[\"Carbohydrates (g)\"], bins=5, kde=False, color=\"blue\", ax=axes[0, 0])\n",
    "axes[0, 0].set_xlabel(\"Carbohydrates (g)\")\n",
    "axes[0, 0].set_ylabel(\"Frequency\")\n",
    "\n",
    "sns.histplot(df[\"Fats (g)\"], bins=5, kde=False, color=\"red\", ax=axes[0, 1])\n",
    "axes[0, 1].set_xlabel(\"Fats (g)\")\n",
    "axes[0, 1].set_ylabel(\"Frequency\")\n",
    "\n",
    "sns.histplot(df[\"Proteins (g)\"], bins=5, kde=False, color=\"magenta\", ax=axes[1, 0])\n",
    "axes[1, 0].set_xlabel(\"Proteins (g)\")\n",
    "axes[1, 0].set_ylabel(\"Frequency\")\n",
    "\n",
    "sns.histplot(df[\"Sodium (mg)\"], bins=5, kde=False, color=\"yellow\", ax=axes[1, 1])\n",
    "axes[1, 1].set_xlabel(\"Sodium (mg)\")\n",
    "axes[1, 1].set_ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Calories (kcal)  Carbohydrates (g)  Fats (g)  Proteins (g)  Sodium (mg)\n",
      "0         0.217207           0.272917  0.042424      0.144444     0.071111\n",
      "1         0.112613           0.194444  0.000000      0.000000     0.066667\n",
      "2         0.213964           0.190972  0.060606      0.202020     0.193889\n",
      "3         0.469189           0.208333  0.206061      0.501010     0.226311\n",
      "4         0.646928           0.450347  0.194848      0.752323     0.217618\n"
     ]
    }
   ],
   "source": [
    "# Normalize the data with Single Feature Scaling\n",
    "df_norm = df / df.max()\n",
    "print(df_norm.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See the correlation between the variables to consider the variables with the highest correlation\n",
    "sns.heatmap(df_norm.corr(), annot=True, cmap=\"RdBu_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Carbohydrates (g)  Fats (g)  Proteins (g)  Sodium (mg)\n",
      "0           0.272917  0.042424      0.144444     0.071111\n",
      "1           0.194444  0.000000      0.000000     0.066667\n",
      "2           0.190972  0.060606      0.202020     0.193889\n",
      "3           0.208333  0.206061      0.501010     0.226311\n",
      "4           0.450347  0.194848      0.752323     0.217618\n"
     ]
    }
   ],
   "source": [
    "# Select the x (carbohydrates, fats, proteins, sodium) and y (calories) variables\n",
    "variables = []\n",
    "for column in df_norm.columns:\n",
    "    if column != \"Calories (kcal)\":\n",
    "        variables.append(column)\n",
    "x = df_norm[variables]\n",
    "y = df_norm[\"Calories (kcal)\"]\n",
    "print(x.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 336 rows in the train set\n",
      "There are 84 rows in the test set\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train and test\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=84) # 84 is the seed\n",
    "print(f\"There are {len(x_train)} rows in the train set\")\n",
    "print(f\"There are {len(x_test)} rows in the test set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation is 0.870791129858678\n",
      "The R2 is 0.870791129858678\n",
      "The coefficients are:\n",
      "0.6374 for Carbohydrates\n",
      "0.0707 for Fats\n",
      "0.4634 for Proteins\n",
      "0.0055 for Sodium\n",
      "0.0 for the Intercept\n",
      "So formula is Calories = 0.6374*x1 + 0.0707*x2 + 0.4634*x3 + 0.0055*x4 + 0.0\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "linreg = linear_model.LinearRegression(fit_intercept=False) # False because if there isn't consumption there aren't calories\n",
    "linreg.fit(x_train, y_train)\n",
    "y_pred = linreg.predict(x_test)\n",
    "correlation = linreg.score(x_test, y_test)\n",
    "r2 = metrics.r2_score(y_test, y_pred)\n",
    "print(f\"The correlation is {correlation}\")\n",
    "print(f\"The R2 is {r2}\")\n",
    "print(\"The coefficients are:\")\n",
    "print(f\"{linreg.coef_[0]:.4f} for Carbohydrates\")\n",
    "print(f\"{linreg.coef_[1]:.4f} for Fats\")\n",
    "print(f\"{linreg.coef_[2]:.4f} for Proteins\")\n",
    "print(f\"{linreg.coef_[3]:.4f} for Sodium\")\n",
    "print(f\"{linreg.intercept_} for the Intercept\")\n",
    "print(f\"So formula is Calories = {linreg.coef_[0]:.4f}*x1 + {linreg.coef_[1]:.4f}*x2 + {linreg.coef_[2]:.4f}*x3 + {linreg.coef_[3]:.4f}*x4 + {linreg.intercept_}\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 ('venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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