Housing_California.ipynb

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   "cell_type": "markdown",
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   "metadata": {
    "tags": []
   },
   "source": [
    "### Conjunto de datos de precios de viviendas de California"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4e914bb-d0ac-44ba-82c5-fdb634578d1c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "# Modelos de Regresión\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f866c48-ec59-46e1-8957-4e5a5d87c285",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "housing = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac11fdd8-cf60-4b31-8bdc-b5e71d8e3690",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "867cfe58-e920-47b4-844f-3109ec7e42c1",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
       "          37.88      , -122.23      ],\n",
       "       [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
       "          37.86      , -122.22      ],\n",
       "       [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
       "          37.85      , -122.24      ],\n",
       "       ...,\n",
       "       [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
       "          39.43      , -121.22      ],\n",
       "       [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
       "          39.43      , -121.32      ],\n",
       "       [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
       "          39.37      , -121.24      ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "housing.data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "92c86714-008a-4328-bb90-c39b5e54136d",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32088302-7909-4c57-8c58-ff950f407ff0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.data.shape # nos esta diciendo que hay 20640 casas con 8 caracteristicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f982fc88-a262-463a-b650-f8a9931e3ade",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.target.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5ff69a68-8b13-46b7-a756-00dab28cab65",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test =train_test_split(housing.data, housing.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a504f320-ce2d-44a6-93d2-e28f3fe22eb9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15480, 8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape #data para entreb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6d9c049c-6ccf-4bf9-9532-c28c22422b2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5160, 8)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape # para hacer testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6506e049-e4ae-40c4-b3f3-06a196dbb80b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsRegressor(n_neighbors = 7) # vamos a considerar a los 7 vecinos mas cercanos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "302f4265-3269-4f3d-ba03-c9d7c936309a",
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       "KNeighborsRegressor(n_neighbors=7)"
      ]
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     "execution_count": 30,
     "metadata": {},
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    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
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   "cell_type": "code",
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   "id": "9f6e4f2e-9d2d-41dc-824c-6ca907429959",
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    "knn.score(X_test,y_test) #\"Apredio muy mal\" solo hizo prediccion en un 0,14 de las veces"
   ]
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   "source": [
    "knn = KNeighborsRegressor(n_neighbors = 10) # vamos a considerar a los 10 vecinos mas cercanos a ver si mejora la prediccion"
   ]
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      ],
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=10)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a7093618-7dba-4bfd-b6d6-e432476178d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "0.15149637086379142"
      ]
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     "execution_count": 61,
     "metadata": {},
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   "source": [
    "knn.score(X_test,y_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdfa34ce-773d-4cc5-8a29-32243f64b904",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Ahora vamos hacerlo con Regresion lineal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "af620692-9c37-4a98-9f3d-caf0c3c3f08e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "del knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "043a23cf-9f7a-4627-a7d1-85130d25d23f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rl= LinearRegression ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "260846f3-5ce8-491d-a544-69b3001ef579",
   "metadata": {
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       "<style>#sk-container-id-14 {color: black;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" 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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rl.fit (X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6f9ad84c-f95e-41b7-9370-e74e26072657",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5766997025821846"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rl.score(X_test,y_test) # una predicion de algo mas del 50%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c217e8f-2a44-490a-9ae3-2a8f8da94155",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Vamos hacerlo con Ridge "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a29441bd-ddb0-44d0-9aa5-b881b8790fce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ridge =  Ridge (alpha = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "955b17c9-5436-4356-b662-6fec8d2b8b17",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 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-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 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-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 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-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 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-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=3)</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-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=3)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "35f6167d-5a5c-4580-83d5-6f0c4b0340cf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.576683999171477"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.score(X_test,y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "523da411-61d6-4e74-8770-faed0f12f00b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57bb2ec3-791d-4fa2-bd98-c9437687c945",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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