   "cell_type": "markdown",
   "metadata": {
   "colab_type": "text",
   "id": "25COd2is4XNu"
   },
   "source": [
   "**Q3 Why is a Sigmoid function / Softmax function is used ?** \n",
   "\n",
   "In classification problems, we deal with the probability in predicting the sample belongs to the particular class. Moreover, Sigmoid or Softmax functions are dealt with probabilities. \n",
   "\n",
   " 1.Sum of the output of all probabilities of sigmoid/softmax is equal to one. \n",
   " \n",
   " 2.Softmax is used for multi-class problems whereas sigmoid is for binary class. The Softmax function is basically a generalization of the sigmoid function.\n",
   " \n",
   " 3.As probability lies between 0 and 1. Sigmoid tries to squash the output between 0 and 1, predicting the probability of class. \n",
   "\n",
   "**Figure :**\n",
   "\n",
   "You cannot use the graph as a replacement for binary classification. It is not differentiable as we can’t find the slope between the points from 0 to 1. The slope is infinity or undefined.\n",
   "\n"
   ]
},
{
   "cell_type": "markdown",
   "metadata": {
   "colab_type": "text",
   "id": "OtPZ-ndV64eJ"
   },
   "source": [
   "**Q4 - House Boston Regression problem**"
   ]
},
{
   "cell_type": "code",
   "metadata": {
   "colab_type": "code",
   "id": "u23xAQdDz_qu",
   "outputId": "9a3024a8-8b6b-4480-efce-9a2dccc600b6",
   "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 34
   }
   },
   "source": [
   "#Loading the required libraries\n",
   "\n",
   "import pandas as pd\n",
   "import numpy as np\n",
   "import seaborn as sns\n",
   "import matplotlib.pyplot as plt\n",
   "from sklearn.model_selection import train_test_split\n",
   "from sklearn.linear_model import LinearRegression\n",
   "from sklearn import metrics\n",
   "from sklearn.metrics import r2_score\n",
   "\n",
   "\n",
   "from sklearn.datasets import load_boston\n",
   "dataset = load_boston()\n",
   "\n",
   "dataset.keys()"
   ],
   "execution_count": 0,
   "outputs": [
   {
      "output_type": "execute_result",
      "data": {
         "text/plain": [
         "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
         ]
      },
      "metadata": {
         "tags": []
      },
      "execution_count": 1
   }
   ]
},
{
   "cell_type": "markdown",
   "metadata": {
   "colab_type": "text",
   "id": "p-3pvXkgumar"
   },
   "source": [
   "**Exploratory data analysis -> EDA**"
   ]
},
{
   "cell_type": "code",
   "metadata": {
   "colab_type": "code",
   "id": "E0x8hGMV2-G5",
   "outputId": "b95d2339-2886-4f4b-9336-a286f5098cde",
   "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 34
   }
   },
   "source": [
   "type(dataset.data), dataset.data.shape #type and shape of the data"
   ],
   "execution_count": 0,
   "outputs": [
   {
      "output_type": "execute_result",
      "data": {
         "text/plain": [
         "(numpy.ndarray, (506, 13))"
         ]
      },
      "metadata": {
         "tags": []
      },
      "execution_count": 2
   }
   ]
},
{
   "cell_type": "code",
   "metadata": {
   "colab_type": "code",
   "id": "AgTN1uOX3JQV",
   "outputId": "98a47260-d36a-438c-c911-6e61308848eb",
   "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 34
   }
   },
   "source": [
   "type(dataset.target), dataset.target.shape #type and shape of the target variable"
   ],
   "execution_count": 0,
   "outputs": [
   {
      "output_type": "execute_result",
      "data": {
         "text/plain": [
         "(numpy.ndarray, (506,))"
         ]
      },
      "metadata": {
         "tags": []
      },
      "execution_count": 3
   }
   ]
},
{
   "cell_type": "code",
   "metadata": {
   "colab_type": "code",
   "id": "wPiujZ833OGk",
   "outputId": "c1832291-dbf7-4d24-c38b-9fbe64057886",
   "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 51
   }
   },
   "source": [
   "dataset['feature_names']       # Columns of the data - features names"
   ],
   "execution_count": 0,
   "outputs": [
   {
      "output_type": "execute_result",
      "data": {
         "text/plain": [
         "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
         "    'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
         ]
      },
      "metadata": {
         "tags": []
      },
      "execution_count": 4
   }
   ]
},
{
   "cell_type": "code",
   "metadata": {
   "colab_type": "code",
   "id": "rAf3Jrc60B-_",
   "colab": {}
   },
   "source": [
   "df = pd.DataFrame(dataset.data, columns=dataset.feature_names) #loading data into pandas as a dataframe\n",
   "df.columns = df.columns.str.lower().str.rstrip()          #lowering the column names of the dataframe"
   ],
   "execution_count": 0,
   "outputs": []
},
{
   "cell_type": "code",
   "metadata": {
   "colab_type": "code",
   "id": "xvmyuTor0h2n",
   "colab": {}
   },
   "source": [
   "df['price'] = dataset.target       #appending the target variable to the dataframe"
   ],
   "execution_count": 0,
   "outputs": []
},
{
   "cell_type": "code",
   "metadata": {
   "colab_type": "code",
   "id": "PFFvMJ4Q30Nz",
   "outputId": "e17e3912-6664-45e0-ba78-c75f3aee7aac",
   "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 34
   }
   },
   "source": [
   "df.shape       #shape of the dataframe"
   ],
   "execution_count": 0,
   "outputs": [
   {
      "output_type": "execute_result",
      "data": {
         "text/plain": [
         "(506, 14)"
         ]
      },
      "metadata": {
         "tags": []
      },
      "execution_count": 7
   }
   ]
},
{
   "cell_type": "code",
   "metadata": {
   "colab_type": "code",
   "id": "lHhF2RZX3dnW",
   "outputId": "1089d4d1-7922-4acd-ed95-c46a560bf67d",
   "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 204
   }
   },
   "source": [
   "df.head()    #starting 5 rows of the dataframe"
   ],
   "execution_count": 0,
   "outputs": [
   {
      "output_type": "execute_result",
      "data": {
         "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

Overview: There's nothing intrinsically different or magical abo...