{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyMe/qH0fji9q4qVq7a3X5Qd"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"yg4sb7MFTDpX","executionInfo":{"status":"ok","timestamp":1701181342541,"user_tz":180,"elapsed":3,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"outputs":[],"source":["# Crear un modelo de regresion lineal que sea capaz de convertir grados celcius a fahrenheit"]},{"cell_type":"code","source":["import pandas as pd\n","temperatura = pd.read_csv(\"datos_temperatura.csv\")\n","temperatura.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uGxps61UTz-H","executionInfo":{"status":"ok","timestamp":1701181800426,"user_tz":180,"elapsed":292,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"6b14bd26-96c9-4d5c-95c5-d8a360a0e50d"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 20 entries, 0 to 19\n","Data columns (total 2 columns):\n"," #   Column      Non-Null Count  Dtype  \n","---  ------      --------------  -----  \n"," 0   Fahrenheit  20 non-null     int64  \n"," 1   Celsius     20 non-null     float64\n","dtypes: float64(1), int64(1)\n","memory usage: 448.0 bytes\n"]}]},{"cell_type":"code","source":["# visualizacion de los datos\n","import seaborn as sb\n","sb.scatterplot(x=\"Fahrenheit\" , y=\"Celsius\" , data=temperatura,\n","               hue=\"Fahrenheit\" , palette=\"coolwarm\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"aAdsm_DZVEyV","executionInfo":{"status":"ok","timestamp":1701182101563,"user_tz":180,"elapsed":786,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"a6313967-5392-4efd-af1a-ef8cd2d12b0c"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Fahrenheit', ylabel='Celsius'>"]},"metadata":{},"execution_count":12},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# preparacion de los datos\n","# caracteristicas X , etiqueta y\n","X = temperatura[\"Celsius\"]\n","y = temperatura[\"Fahrenheit\"]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wvjdaB_mV3UL","executionInfo":{"status":"ok","timestamp":1701182241854,"user_tz":180,"elapsed":3,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"fd2bfee4-ddbc-4125-e70e-2573a3d49526"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(0     16.111111\n"," 1     -5.000000\n"," 2     10.000000\n"," 3     33.333333\n"," 4      7.777778\n"," 5    -47.777778\n"," 6     -3.888889\n"," 7    -18.333333\n"," 8    -28.888889\n"," 9    -40.555556\n"," 10   -16.111111\n"," 11   -18.333333\n"," 12    -4.444444\n"," 13   -55.555556\n"," 14    -0.555556\n"," 15    21.111111\n"," 16   -62.222222\n"," 17   -44.444444\n"," 18     2.222222\n"," 19   -49.444444\n"," Name: Celsius, dtype: float64,\n"," 0     61\n"," 1     23\n"," 2     50\n"," 3     92\n"," 4     46\n"," 5    -54\n"," 6     25\n"," 7     -1\n"," 8    -20\n"," 9    -41\n"," 10     3\n"," 11    -1\n"," 12    24\n"," 13   -68\n"," 14    31\n"," 15    70\n"," 16   -80\n"," 17   -48\n"," 18    36\n"," 19   -57\n"," Name: Fahrenheit, dtype: int64)"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["X.values\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5bGiDsXgXjdB","executionInfo":{"status":"ok","timestamp":1701182334042,"user_tz":180,"elapsed":3,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"ac0bfb83-ebc4-44b2-bfdb-77c13a5df7e0"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 16.11111111,  -5.        ,  10.        ,  33.33333333,\n","         7.77777778, -47.77777778,  -3.88888889, -18.33333333,\n","       -28.88888889, -40.55555556, -16.11111111, -18.33333333,\n","        -4.44444444, -55.55555556,  -0.55555556,  21.11111111,\n","       -62.22222222, -44.44444444,   2.22222222, -49.44444444])"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["# transformacion a un arreglo en cada posicion de los datos\n","X_procesada = X.values.reshape(-1,1)\n","y_procesada = y.values.reshape(-1,1)"],"metadata":{"id":"IHZLii2NXk_h","executionInfo":{"status":"ok","timestamp":1701182480749,"user_tz":180,"elapsed":298,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["# Creacion del modelo\n","from sklearn.linear_model import LinearRegression\n","modelo = LinearRegression()\n","\n"],"metadata":{"id":"BfJVb0UaX4HA","executionInfo":{"status":"ok","timestamp":1701182610990,"user_tz":180,"elapsed":290,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["# entrenamiento del modelo\n","modelo.fit(X_procesada , y_procesada)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"h2L6sNwRYdYq","executionInfo":{"status":"ok","timestamp":1701182612579,"user_tz":180,"elapsed":295,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"285d3827-6d8b-4f58-a822-a91564e5c733"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["# Prediccion\n","C = int(input(\"Ingresa la cantidad de grados Celcius para Convertir : \"))\n","prediccion = modelo.predict([[C]])\n","print(f\"La prediccion de {C} grados Celcius es de {prediccion} Fahrenheit\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JvkY82HSYkQh","executionInfo":{"status":"ok","timestamp":1701183226097,"user_tz":180,"elapsed":3012,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"3556183f-e2b6-48d8-cbca-7700d40d7995"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["Ingresa la cantidad de grados Celcius para Convertir : 22\n","La prediccion de 22 grados Celcius es de [[71.6]] Fahrenheit\n"]}]},{"cell_type":"code","source":["# puntaje de aprendizaje del modelo (0 a 1)\n","modelo.score(X_procesada , y_procesada)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E7CRqCBxaX6v","executionInfo":{"status":"ok","timestamp":1701183102248,"user_tz":180,"elapsed":3,"user":{"displayName":"Horacio Laphitz","userId":"05970776226255252387"}},"outputId":"ddf7456a-bba0-45cb-8b65-556372e5465e"},"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{},"execution_count":35}]},{"cell_type":"code","source":[],"metadata":{"id":"1V7VV-0NacFQ"},"execution_count":null,"outputs":[]}]}