diff --git a/Chapter01/keras.ipynb b/Chapter01/keras.ipynb
new file mode 100644
index 0000000..43469c0
--- /dev/null
+++ b/Chapter01/keras.ipynb
@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential \n",
+    "from keras.layers import Dense\n",
+    "from keras import optimizers\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Layer 1\n",
+    "model.add(Dense(units = 4, activation='sigmoid', input_dim = 3))\n",
+    "# Output layer\n",
+    "model.add(Dense(units=1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_1 (Dense)              (None, 4)                 16        \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 1)                 5         \n",
+      "=================================================================\n",
+      "Total params: 21\n",
+      "Trainable params: 21\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loss function keras\n",
+    "sgd = optimizers.SGD(lr=1)\n",
+    "model.compile(loss = 'mean_squared_error', optimizer=sgd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.03039777]\n",
+      " [0.9787922 ]\n",
+      " [0.9621813 ]\n",
+      " [0.03268841]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = np.array([[0,0,1],\n",
+    "              [0,1,1],\n",
+    "              [1,0,1],\n",
+    "              [1,1,1]])\n",
+    "y = np.array([[0],[1],[1],[0]])\n",
+    "\n",
+    "# trainer model with fit\n",
+    "model.fit(X, y, epochs=1500, verbose=False)\n",
+    "\n",
+    "print(model.predict(X))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neural-network-projects-python",
+   "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": "3.6.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Chapter01/pandas.ipynb b/Chapter01/pandas.ipynb
new file mode 100644
index 0000000..f5b8e5e
--- /dev/null
+++ b/Chapter01/pandas.ipynb
@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "matplotlib.use(\"TkAgg\")\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\",\n",
+    "                names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 150 entries, 0 to 149\n",
+      "Data columns (total 5 columns):\n",
+      "sepal_length    150 non-null float64\n",
+      "sepal_width     150 non-null float64\n",
+      "petal_length    150 non-null float64\n",
+      "petal_width     150 non-null float64\n",
+      "class           150 non-null object\n",
+      "dtypes: float64(4), object(1)\n",
+      "memory usage: 5.9+ KB\n",
+      "None\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get info of the data\n",
+    "print(df.info())\n",
+    "print('')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       sepal_length  sepal_width  petal_length  petal_width\n",
+      "count    150.000000   150.000000    150.000000   150.000000\n",
+      "mean       5.843333     3.054000      3.758667     1.198667\n",
+      "std        0.828066     0.433594      1.764420     0.763161\n",
+      "min        4.300000     2.000000      1.000000     0.100000\n",
+      "25%        5.100000     2.800000      1.600000     0.300000\n",
+      "50%        5.800000     3.000000      4.350000     1.300000\n",
+      "75%        6.400000     3.300000      5.100000     1.800000\n",
+      "max        7.900000     4.400000      6.900000     2.500000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get statistical summary of the data\n",
+    "print(df.describe())\n",
+    "print('')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal_length  sepal_width  petal_length  petal_width        class\n",
+      "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
+      "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
+      "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
+      "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
+      "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
+      "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
+      "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
+      "7           5.0          3.4           1.5          0.2  Iris-setosa\n",
+      "8           4.4          2.9           1.4          0.2  Iris-setosa\n",
+      "9           4.9          3.1           1.5          0.1  Iris-setosa\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    sepal_length  sepal_width  petal_length  petal_width        class\n",
+      "0            5.1          3.5           1.4          0.2  Iris-setosa\n",
+      "5            5.4          3.9           1.7          0.4  Iris-setosa\n",
+      "10           5.4          3.7           1.5          0.2  Iris-setosa\n",
+      "14           5.8          4.0           1.2          0.2  Iris-setosa\n",
+      "15           5.7          4.4           1.5          0.4  Iris-setosa\n",
+      "16           5.4          3.9           1.3          0.4  Iris-setosa\n",
+      "17           5.1          3.5           1.4          0.3  Iris-setosa\n",
+      "18           5.7          3.8           1.7          0.3  Iris-setosa\n",
+      "19           5.1          3.8           1.5          0.3  Iris-setosa\n",
+      "20           5.4          3.4           1.7          0.2  Iris-setosa\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select rows with sepal_length more than 5.0\n",
+    "# the loc command allows to access a grou of rows an columns\n",
+    "df2 = df.loc[df['sepal_length'] > 5.0, ]\n",
+    "print(df2.head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "marker_shapes = ['.', '^','*']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, species in enumerate(df['class'].unique()):\n",
+    "  if i == 0:\n",
+    "    ax = df[df['class'] == species].plot.scatter(x='sepal_length', y='sepal_width', marker=marker_shapes[i], s=100,title=\"Sepal Width vs Length by Species\", label=species, figsize=(10,7))\n",
+    "  else:\n",
+    "    df[df['class'] == species].plot.scatter(x='sepal_length', y='sepal_width', marker=marker_shapes[i], s=100, title=\"Sepal Width vs Length by Species\", label=species, ax=ax)\n",
+    "# plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1b5ac746f28>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['petal_length'].plot.hist(title = 'Histogram of Petal length')\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.plot.box(title = 'Boxplot length and width of sepal and length and width of petal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Encoding of categorical var\n",
+    "\n",
+    "### One common way to convert these categorical variables into numerical variables is a technique known as one-hot encoding, implemented by the get_dummies() function in pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Day_Friday  Day_Monday  Day_Saturday  Day_Sunday  Day_Thursday  \\\n",
+      "0           0           1             0           0             0   \n",
+      "1           0           0             0           0             0   \n",
+      "2           0           0             0           0             0   \n",
+      "3           0           0             0           0             1   \n",
+      "4           1           0             0           0             0   \n",
+      "5           0           0             1           0             0   \n",
+      "6           0           0             0           1             0   \n",
+      "\n",
+      "   Day_Tuesday  Day_Wednesday  \n",
+      "0            0              0  \n",
+      "1            1              0  \n",
+      "2            0              1  \n",
+      "3            0              0  \n",
+      "4            0              0  \n",
+      "5            0              0  \n",
+      "6            0              0  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df2 = pd.DataFrame({'Day': ['Monday','Tuesday','Wednesday',\n",
+    "                           'Thursday','Friday','Saturday',\n",
+    "                           'Sunday']})\n",
+    "\n",
+    "print(pd.get_dummies(df2))\n",
+    "print('')\n",
+    "                           "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imputing missing values\n",
+    "# Import the iris data once again\n",
+    "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\",\n",
+    "                names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_index = np.random.choice(df.index, replace = False, size = 10)\n",
+    "df.loc[random_index, 'sepal_length'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sepal_length     True\n",
+      "sepal_width     False\n",
+      "petal_length    False\n",
+      "petal_width     False\n",
+      "class           False\n",
+      "dtype: bool\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check where the missing values are\n",
+    "print(df.isnull().any())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows before deleting: 150\n",
+      "Number of rows after deleting: 140\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Drop missing values\n",
+    "print(\"Number of rows before deleting: %d\" % (df.shape[0]))\n",
+    "df2 = df.dropna()\n",
+    "print(\"Number of rows after deleting: %d\" % (df2.shape[0]))\n",
+    "print('')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neural-network-projects-python",
+   "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": "3.6.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Chapter01/teste1_train_neural.ipynb b/Chapter01/teste1_train_neural.ipynb
new file mode 100644
index 0000000..31b7c17
--- /dev/null
+++ b/Chapter01/teste1_train_neural.ipynb
@@ -0,0 +1,108 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoid(x):\n",
+    "    return 1/(1 + np.exp(-x))\n",
+    "\n",
+    "def sigmoid_derivate(x):\n",
+    "    return x * (1 - x) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NeuralNetwork:\n",
+    "    def __init__(self, x, y):\n",
+    "        self.input = x\n",
+    "        # matriz numpy com vários valores aleatórios\n",
+    "        self.weight1 = np.random.rand(self.input.shape[1], 4)\n",
+    "        self.weight2 = np.random.rand(4, 1)\n",
+    "        self.y = y\n",
+    "        self.output = np.zeros(self.y.shape)\n",
+    "    # definindo uma feedforward para calcualr a saída prevista\n",
+    "    def feedforward(self):\n",
+    "        self.layer1 = sigmoid(np.dot(self.input, self.weight1))\n",
+    "        self.output = sigmoid(np.dot(self.layer1, self.weight2))\n",
+    "\n",
+    "    def backprop(self):\n",
+    "        d_weight2 = np.dot(self.layer1.T, (2*(self.y - self.output) * sigmoid_derivate(self.output)))\n",
+    "        d_weight1 = np.dot(self.input.T, (np.dot(2*(self.y - self.output) * sigmoid_derivate(self.output), self.weight2.T) * sigmoid_derivate(self.layer1)))\n",
+    "\n",
+    "        self.weight1 += d_weight1\n",
+    "        self.weight2 += d_weight2\n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.00935196]\n",
+      " [0.97232138]\n",
+      " [0.97279786]\n",
+      " [0.03390555]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == \"__main__\":\n",
+    "    X = np.array([[0, 0, 1],\n",
+    "                 [0, 1, 1],\n",
+    "                 [1, 0, 1],\n",
+    "                 [1, 1, 1]])\n",
+    "    \n",
+    "    y = np.array([[0], [1], [1], [0]])\n",
+    "    nn = NeuralNetwork(X, y)\n",
+    "\n",
+    "    for i in range(1500):\n",
+    "        nn.feedforward()\n",
+    "        nn.backprop()\n",
+    "    print(nn.output)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neural-network-projects-python",
+   "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": "3.6.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Chapter02/__pycache__/utils.cpython-36.pyc b/Chapter02/__pycache__/utils.cpython-36.pyc
new file mode 100644
index 0000000..fe5aa8e
Binary files /dev/null and b/Chapter02/__pycache__/utils.cpython-36.pyc differ
diff --git a/Chapter02/diabete.csv b/Chapter02/diabete.csv
new file mode 100644
index 0000000..9e6a362
--- /dev/null
+++ b/Chapter02/diabete.csv
@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
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+0,99,0,0,0,25,0.253,22,0
+4,127,88,11,155,34.5,0.598,28,0
+4,118,70,0,0,44.5,0.904,26,0
+2,122,76,27,200,35.9,0.483,26,0
+6,125,78,31,0,27.6,0.565,49,1
+1,168,88,29,0,35,0.905,52,1
+2,129,0,0,0,38.5,0.304,41,0
+4,110,76,20,100,28.4,0.118,27,0
+6,80,80,36,0,39.8,0.177,28,0
+10,115,0,0,0,0,0.261,30,1
+2,127,46,21,335,34.4,0.176,22,0
+9,164,78,0,0,32.8,0.148,45,1
+2,93,64,32,160,38,0.674,23,1
+3,158,64,13,387,31.2,0.295,24,0
+5,126,78,27,22,29.6,0.439,40,0
+10,129,62,36,0,41.2,0.441,38,1
+0,134,58,20,291,26.4,0.352,21,0
+3,102,74,0,0,29.5,0.121,32,0
+7,187,50,33,392,33.9,0.826,34,1
+3,173,78,39,185,33.8,0.97,31,1
+10,94,72,18,0,23.1,0.595,56,0
+1,108,60,46,178,35.5,0.415,24,0
+5,97,76,27,0,35.6,0.378,52,1
+4,83,86,19,0,29.3,0.317,34,0
+1,114,66,36,200,38.1,0.289,21,0
+1,149,68,29,127,29.3,0.349,42,1
+5,117,86,30,105,39.1,0.251,42,0
+1,111,94,0,0,32.8,0.265,45,0
+4,112,78,40,0,39.4,0.236,38,0
+1,116,78,29,180,36.1,0.496,25,0
+0,141,84,26,0,32.4,0.433,22,0
+2,175,88,0,0,22.9,0.326,22,0
+2,92,52,0,0,30.1,0.141,22,0
+3,130,78,23,79,28.4,0.323,34,1
+8,120,86,0,0,28.4,0.259,22,1
+2,174,88,37,120,44.5,0.646,24,1
+2,106,56,27,165,29,0.426,22,0
+2,105,75,0,0,23.3,0.56,53,0
+4,95,60,32,0,35.4,0.284,28,0
+0,126,86,27,120,27.4,0.515,21,0
+8,65,72,23,0,32,0.6,42,0
+2,99,60,17,160,36.6,0.453,21,0
+1,102,74,0,0,39.5,0.293,42,1
+11,120,80,37,150,42.3,0.785,48,1
+3,102,44,20,94,30.8,0.4,26,0
+1,109,58,18,116,28.5,0.219,22,0
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+12,100,84,33,105,30,0.488,46,0
+1,147,94,41,0,49.3,0.358,27,1
+1,81,74,41,57,46.3,1.096,32,0
+3,187,70,22,200,36.4,0.408,36,1
+6,162,62,0,0,24.3,0.178,50,1
+4,136,70,0,0,31.2,1.182,22,1
+1,121,78,39,74,39,0.261,28,0
+3,108,62,24,0,26,0.223,25,0
+0,181,88,44,510,43.3,0.222,26,1
+8,154,78,32,0,32.4,0.443,45,1
+1,128,88,39,110,36.5,1.057,37,1
+7,137,90,41,0,32,0.391,39,0
+0,123,72,0,0,36.3,0.258,52,1
+1,106,76,0,0,37.5,0.197,26,0
+6,190,92,0,0,35.5,0.278,66,1
+2,88,58,26,16,28.4,0.766,22,0
+9,170,74,31,0,44,0.403,43,1
+9,89,62,0,0,22.5,0.142,33,0
+10,101,76,48,180,32.9,0.171,63,0
+2,122,70,27,0,36.8,0.34,27,0
+5,121,72,23,112,26.2,0.245,30,0
+1,126,60,0,0,30.1,0.349,47,1
+1,93,70,31,0,30.4,0.315,23,0
\ No newline at end of file
diff --git a/Chapter02/main.py b/Chapter02/main.py
index 9effd70..375c129 100644
--- a/Chapter02/main.py
+++ b/Chapter02/main.py
@@ -13,7 +13,7 @@
 np.random.seed(16)
 
 try:
-    df = pd.read_csv('diabetes.csv')
+    df = pd.read_csv('diabete.csv')
 except:
     print("""
       Dataset not found in your computer.
diff --git a/Chapter02/teste2.ipynb b/Chapter02/teste2.ipynb
new file mode 100644
index 0000000..2a665b2
--- /dev/null
+++ b/Chapter02/teste2.ipynb
@@ -0,0 +1,985 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.model_selection import train_test_split # this function allows you to randomly split a datafarme\n",
+    "from sklearn.metrics import confusion_matrix, roc_curve # this function allows the visualization tool that analysis\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('diabete.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+      "0            6      148             72             35        0  33.6   \n",
+      "1            1       85             66             29        0  26.6   \n",
+      "2            8      183             64              0        0  23.3   \n",
+      "3            1       89             66             23       94  28.1   \n",
+      "4            0      137             40             35      168  43.1   \n",
+      "\n",
+      "   DiabetesPedigreeFunction  Age  Outcome  \n",
+      "0                     0.627   50        1  \n",
+      "1                     0.351   31        0  \n",
+      "2                     0.672   32        1  \n",
+      "3                     0.167   21        0  \n",
+      "4                     2.288   33        1  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x720 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(figsize=(15,10), color = 'y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Selected KDE bandwidth is 0. Cannot estimate density.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mkdensityfft\u001b[1;34m(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'scott'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-b3854d9c3503>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0midx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutcome\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkde_kws\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'linestyle'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'-'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'color'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m\"No Diabetes\"\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutcome\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkde_kws\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'linestyle'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'--'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'color'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'label'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'Diabetes'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[1;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)\u001b[0m\n\u001b[0;32m    229\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    230\u001b[0m         \u001b[0mkde_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 231\u001b[1;33m         \u001b[0mkdeplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvertical\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkde_color\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkde_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    232\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkde_color\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m             \u001b[0mkde_kws\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"color\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde_color\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[0;32m    689\u001b[0m         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,\n\u001b[0;32m    690\u001b[0m                                  \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 691\u001b[1;33m                                  cumulative=cumulative, **kwargs)\n\u001b[0m\u001b[0;32m    692\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    693\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_univariate_kdeplot\u001b[1;34m(data, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)\u001b[0m\n\u001b[0;32m    281\u001b[0m         x, y = _statsmodels_univariate_kde(data, kernel, bw,\n\u001b[0;32m    282\u001b[0m                                            \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 283\u001b[1;33m                                            cumulative=cumulative)\n\u001b[0m\u001b[0;32m    284\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m         \u001b[1;31m# Fall back to scipy if missing statsmodels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_statsmodels_univariate_kde\u001b[1;34m(data, kernel, bw, gridsize, cut, clip, cumulative)\u001b[0m\n\u001b[0;32m    353\u001b[0m     \u001b[0mfft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkernel\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"gau\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    354\u001b[0m     \u001b[0mkde\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msmnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mKDEUnivariate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 355\u001b[1;33m     \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    356\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m         \u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, kernel, bw, fft, weights, gridsize, adjust, cut, clip)\u001b[0m\n\u001b[0;32m    138\u001b[0m             density, grid, bw = kdensityfft(endog, kernel=kernel, bw=bw,\n\u001b[0;32m    139\u001b[0m                     \u001b[0madjust\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0madjust\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 140\u001b[1;33m                     clip=clip, cut=cut)\n\u001b[0m\u001b[0;32m    141\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m             density, grid, bw = kdensity(endog, kernel=kernel, bw=bw,\n",
+      "\u001b[1;32mc:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mkdensityfft\u001b[1;34m(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)\u001b[0m\n\u001b[0;32m    451\u001b[0m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    452\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbandwidths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect_bandwidth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkern\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# will cross-val fit this pattern?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    454\u001b[0m     \u001b[0mbw\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0madjust\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\statsmodels\\nonparametric\\bandwidths.py\u001b[0m in \u001b[0;36mselect_bandwidth\u001b[1;34m(x, bw, kernel)\u001b[0m\n\u001b[0;32m    172\u001b[0m         \u001b[1;31m# eventually this can fall back on another selection criterion.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m         \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Selected KDE bandwidth is 0. Cannot estimate density.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mbandwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Selected KDE bandwidth is 0. Cannot estimate density."
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(3, 3, figsize = (15,15))\n",
+    "for idx, col in enumerate(df.columns):\n",
+    "    ax = plt.subplot(3,3,idx + 1)\n",
+    "    ax.yaxis.set_ticklabels([])\n",
+    "    sns.distplot(df.loc[df.Outcome == 0][col], hist=False, axlabel=False, kde_kws={'linestyle':'-', 'color':'black', 'label':\"No Diabetes\"})\n",
+    "    sns.distplot(df.loc[df.Outcome == 1][col], hist=False, axlabel=False, kde_kws={'linestyle':'--', 'color':'black', 'label':'Diabetes'})\n",
+    "    ax.set_title(col)\n",
+    "\n",
+    "plt.subplot(3, 3, 9).set_visible(False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pregnancies                 False\n",
+      "Glucose                     False\n",
+      "BloodPressure               False\n",
+      "SkinThickness               False\n",
+      "Insulin                     False\n",
+      "BMI                         False\n",
+      "DiabetesPedigreeFunction    False\n",
+      "Age                         False\n",
+      "Outcome                     False\n",
+      "dtype: bool\n",
+      "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
+      "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
+      "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
+      "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
+      "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
+      "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
+      "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
+      "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
+      "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
+      "\n",
+      "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
+      "count  768.000000                768.000000  768.000000  768.000000  \n",
+      "mean    31.992578                  0.471876   33.240885    0.348958  \n",
+      "std      7.884160                  0.331329   11.760232    0.476951  \n",
+      "min      0.000000                  0.078000   21.000000    0.000000  \n",
+      "25%     27.300000                  0.243750   24.000000    0.000000  \n",
+      "50%     32.000000                  0.372500   29.000000    0.000000  \n",
+      "75%     36.600000                  0.626250   41.000000    1.000000  \n",
+      "max     67.100000                  2.420000   81.000000    1.000000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verificando se há colunas vazias\n",
+    "print(df.isnull().any())\n",
+    "print(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows with 0 values for each variable\n",
+      "Pregnancies: 111\n",
+      "Glucose: 5\n",
+      "BloodPressure: 35\n",
+      "SkinThickness: 227\n",
+      "Insulin: 374\n",
+      "BMI: 11\n",
+      "DiabetesPedigreeFunction: 0\n",
+      "Age: 0\n",
+      "Outcome: 500\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of rows with 0 values for each variable\")\n",
+    "for col in df.columns:\n",
+    "    missing_rows = df.loc[df[col] == 0].shape[0]\n",
+    "    print(col + \": \" + str(missing_rows))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Glucose'] = df['Glucose'].replace(0, np.nan)\n",
+    "df['BloodPressure'] = df['BloodPressure'].replace(0, np.nan)\n",
+    "df['SkinThickness'] = df['SkinThickness'].replace(0, np.nan)\n",
+    "df['Insulin'] = df['Insulin'].replace(0, np.nan)\n",
+    "df['BMI'] = df['BMI'].replace(0, np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of rows with 0 values for each variable\n",
+      "Pregnancies: 111\n",
+      "Glucose: 0\n",
+      "BloodPressure: 0\n",
+      "SkinThickness: 0\n",
+      "Insulin: 0\n",
+      "BMI: 0\n",
+      "DiabetesPedigreeFunction: 0\n",
+      "Age: 0\n",
+      "Outcome: 500\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of rows with 0 values for each variable\")\n",
+    "for col in df.columns:\n",
+    "    missing_rows = df.loc[df[col] == 0].shape[0]\n",
+    "    print(col + \": \" + str(missing_rows))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Glucose'] = df['Glucose'].fillna(df['Glucose'].mean())\n",
+    "df['BloodPressure'] = df['BloodPressure'].fillna(df['BloodPressure'].mean())\n",
+    "df['SkinThickness'] = df['SkinThickness'].fillna(df['SkinThickness'].mean())\n",
+    "df['Insulin'] = df['Insulin'].fillna(df['Insulin'].mean())\n",
+    "df['BMI'] = df['BMI'].fillna(df['BMI'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+      "mean         0.00     0.00            0.0           0.00     0.00  0.00   \n",
+      "std          1.00     1.00            1.0           1.00     1.00  1.00   \n",
+      "max          3.91     2.54            4.1           7.95     8.13  5.04   \n",
+      "\n",
+      "      DiabetesPedigreeFunction   Age  Outcome  \n",
+      "mean                      0.00  0.00     0.00  \n",
+      "std                       1.00  1.00     1.00  \n",
+      "max                       5.88  4.06     1.37  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by the scale function.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_scaled = preprocessing.scale(df)\n",
+    "df_scaled = pd.DataFrame(df_scaled, columns=df.columns)\n",
+    "df = df_scaled\n",
+    "print(df.describe().loc[['mean', 'std', 'max'],].round(2).abs())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train Test Split Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.loc[:, df.columns != 'Outcome']\n",
+    "y = df.loc[:, 'Outcome']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential() #allows to construt a neural network like lego, stacking layers on top of oe another"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# frist hidden layer : teh functio relu\n",
+    "model.add(Dense(32, activation='relu', input_dim=8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# second hidden layer\n",
+    "model.add(Dense(16, activation='relu'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# output layer\n",
+    "model.add(Dense(1, activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compile model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+      "\n",
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='adam',\n",
+    "              loss='binary_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### let's to train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Crrea\\miniconda3\\envs\\neural-network-projects-python\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
+      "\n",
+      "Epoch 1/200\n",
+      "491/491 [==============================] - 0s 537us/step - loss: 3.9767 - acc: 0.5173\n",
+      "Epoch 2/200\n",
+      "491/491 [==============================] - 0s 49us/step - loss: 2.6069 - acc: 0.4684\n",
+      "Epoch 3/200\n",
+      "491/491 [==============================] - 0s 51us/step - loss: 1.9642 - acc: 0.5051\n",
+      "Epoch 4/200\n",
+      "491/491 [==============================] - 0s 53us/step - loss: 1.5947 - acc: 0.5132\n",
+      "Epoch 5/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 1.2904 - acc: 0.5764\n",
+      "Epoch 6/200\n",
+      "491/491 [==============================] - 0s 53us/step - loss: 1.1050 - acc: 0.5723\n",
+      "Epoch 7/200\n",
+      "491/491 [==============================] - 0s 52us/step - loss: 0.9777 - acc: 0.5845\n",
+      "Epoch 8/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.9061 - acc: 0.5866\n",
+      "Epoch 9/200\n",
+      "491/491 [==============================] - 0s 45us/step - loss: 0.8431 - acc: 0.6212\n",
+      "Epoch 10/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.8101 - acc: 0.6130\n",
+      "Epoch 11/200\n",
+      "491/491 [==============================] - 0s 46us/step - loss: 0.7885 - acc: 0.6273\n",
+      "Epoch 12/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.7594 - acc: 0.6395\n",
+      "Epoch 13/200\n",
+      "491/491 [==============================] - 0s 59us/step - loss: 0.7537 - acc: 0.6477\n",
+      "Epoch 14/200\n",
+      "491/491 [==============================] - 0s 68us/step - loss: 0.6812 - acc: 0.6660\n",
+      "Epoch 15/200\n",
+      "491/491 [==============================] - 0s 49us/step - loss: 0.6702 - acc: 0.6619\n",
+      "Epoch 16/200\n",
+      "491/491 [==============================] - 0s 52us/step - loss: 0.6681 - acc: 0.6843\n",
+      "Epoch 17/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.6446 - acc: 0.6762\n",
+      "Epoch 18/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.6765 - acc: 0.6599\n",
+      "Epoch 19/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.7064 - acc: 0.6375\n",
+      "Epoch 20/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.7197 - acc: 0.6802\n",
+      "Epoch 21/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.7347 - acc: 0.6823\n",
+      "Epoch 22/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.6828 - acc: 0.6538\n",
+      "Epoch 23/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.6297 - acc: 0.6945\n",
+      "Epoch 24/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.6100 - acc: 0.7047\n",
+      "Epoch 25/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.6120 - acc: 0.6945\n",
+      "Epoch 26/200\n",
+      "491/491 [==============================] - 0s 48us/step - loss: 0.6296 - acc: 0.7067\n",
+      "Epoch 27/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.6214 - acc: 0.7006\n",
+      "Epoch 28/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.6029 - acc: 0.6762\n",
+      "Epoch 29/200\n",
+      "491/491 [==============================] - 0s 45us/step - loss: 0.5927 - acc: 0.7108\n",
+      "Epoch 30/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.6468 - acc: 0.6904\n",
+      "Epoch 31/200\n",
+      "491/491 [==============================] - 0s 48us/step - loss: 0.6166 - acc: 0.6925\n",
+      "Epoch 32/200\n",
+      "491/491 [==============================] - 0s 50us/step - loss: 0.5896 - acc: 0.6965\n",
+      "Epoch 33/200\n",
+      "491/491 [==============================] - 0s 45us/step - loss: 0.5980 - acc: 0.7128\n",
+      "Epoch 34/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.6122 - acc: 0.6802\n",
+      "Epoch 35/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.6772 - acc: 0.6558\n",
+      "Epoch 36/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5888 - acc: 0.7047\n",
+      "Epoch 37/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.5726 - acc: 0.7026\n",
+      "Epoch 38/200\n",
+      "491/491 [==============================] - 0s 45us/step - loss: 0.5742 - acc: 0.7189\n",
+      "Epoch 39/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5983 - acc: 0.7210\n",
+      "Epoch 40/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5644 - acc: 0.7230\n",
+      "Epoch 41/200\n",
+      "491/491 [==============================] - 0s 46us/step - loss: 0.6170 - acc: 0.7088\n",
+      "Epoch 42/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5662 - acc: 0.7291\n",
+      "Epoch 43/200\n",
+      "491/491 [==============================] - 0s 49us/step - loss: 0.5821 - acc: 0.7169\n",
+      "Epoch 44/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.6037 - acc: 0.6986\n",
+      "Epoch 45/200\n",
+      "491/491 [==============================] - 0s 52us/step - loss: 0.6189 - acc: 0.7026\n",
+      "Epoch 46/200\n",
+      "491/491 [==============================] - 0s 48us/step - loss: 0.6392 - acc: 0.7230\n",
+      "Epoch 47/200\n",
+      "491/491 [==============================] - 0s 52us/step - loss: 0.6053 - acc: 0.7088\n",
+      "Epoch 48/200\n",
+      "491/491 [==============================] - 0s 44us/step - loss: 0.6845 - acc: 0.6558\n",
+      "Epoch 49/200\n",
+      "491/491 [==============================] - 0s 46us/step - loss: 0.5847 - acc: 0.7108\n",
+      "Epoch 50/200\n",
+      "491/491 [==============================] - 0s 66us/step - loss: 0.6095 - acc: 0.6721\n",
+      "Epoch 51/200\n",
+      "491/491 [==============================] - 0s 64us/step - loss: 0.5686 - acc: 0.7189\n",
+      "Epoch 52/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5835 - acc: 0.7251\n",
+      "Epoch 53/200\n",
+      "491/491 [==============================] - 0s 56us/step - loss: 0.5786 - acc: 0.7210\n",
+      "Epoch 54/200\n",
+      "491/491 [==============================] - 0s 55us/step - loss: 0.5780 - acc: 0.7108\n",
+      "Epoch 55/200\n",
+      "491/491 [==============================] - 0s 45us/step - loss: 0.5681 - acc: 0.7169\n",
+      "Epoch 56/200\n",
+      "491/491 [==============================] - 0s 48us/step - loss: 0.5562 - acc: 0.7169\n",
+      "Epoch 57/200\n",
+      "491/491 [==============================] - 0s 46us/step - loss: 0.5470 - acc: 0.7271\n",
+      "Epoch 58/200\n",
+      "491/491 [==============================] - 0s 49us/step - loss: 0.5416 - acc: 0.7169\n",
+      "Epoch 59/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5546 - acc: 0.7312\n",
+      "Epoch 60/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5510 - acc: 0.7251\n",
+      "Epoch 61/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5544 - acc: 0.7373\n",
+      "Epoch 62/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5682 - acc: 0.7352\n",
+      "Epoch 63/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5449 - acc: 0.7291\n",
+      "Epoch 64/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5530 - acc: 0.6945\n",
+      "Epoch 65/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5541 - acc: 0.7189\n",
+      "Epoch 66/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5460 - acc: 0.7271\n",
+      "Epoch 67/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5442 - acc: 0.7271\n",
+      "Epoch 68/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5344 - acc: 0.7332\n",
+      "Epoch 69/200\n",
+      "491/491 [==============================] - 0s 48us/step - loss: 0.5432 - acc: 0.7251\n",
+      "Epoch 70/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.6122 - acc: 0.7088\n",
+      "Epoch 71/200\n",
+      "491/491 [==============================] - 0s 52us/step - loss: 0.6097 - acc: 0.6986\n",
+      "Epoch 72/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5716 - acc: 0.7149\n",
+      "Epoch 73/200\n",
+      "491/491 [==============================] - 0s 46us/step - loss: 0.5727 - acc: 0.7189\n",
+      "Epoch 74/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5518 - acc: 0.7189\n",
+      "Epoch 75/200\n",
+      "491/491 [==============================] - 0s 38us/step - loss: 0.5368 - acc: 0.7291\n",
+      "Epoch 76/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5519 - acc: 0.7189\n",
+      "Epoch 77/200\n",
+      "491/491 [==============================] - 0s 32us/step - loss: 0.6121 - acc: 0.7026\n",
+      "Epoch 78/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5483 - acc: 0.7230\n",
+      "Epoch 79/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5304 - acc: 0.7230\n",
+      "Epoch 80/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5285 - acc: 0.7332\n",
+      "Epoch 81/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5263 - acc: 0.7434\n",
+      "Epoch 82/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5577 - acc: 0.7251\n",
+      "Epoch 83/200\n",
+      "491/491 [==============================] - 0s 50us/step - loss: 0.5415 - acc: 0.7373\n",
+      "Epoch 84/200\n",
+      "491/491 [==============================] - 0s 67us/step - loss: 0.5613 - acc: 0.7088\n",
+      "Epoch 85/200\n",
+      "491/491 [==============================] - 0s 45us/step - loss: 0.5405 - acc: 0.7454\n",
+      "Epoch 86/200\n",
+      "491/491 [==============================] - 0s 63us/step - loss: 0.5841 - acc: 0.7128\n",
+      "Epoch 87/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5651 - acc: 0.7413\n",
+      "Epoch 88/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5548 - acc: 0.7088\n",
+      "Epoch 89/200\n",
+      "491/491 [==============================] - 0s 44us/step - loss: 0.5473 - acc: 0.7230\n",
+      "Epoch 90/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.5447 - acc: 0.7352\n",
+      "Epoch 91/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5379 - acc: 0.7271\n",
+      "Epoch 92/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5511 - acc: 0.7189\n",
+      "Epoch 93/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5243 - acc: 0.7617\n",
+      "Epoch 94/200\n",
+      "491/491 [==============================] - 0s 38us/step - loss: 0.6233 - acc: 0.7067\n",
+      "Epoch 95/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5738 - acc: 0.7128\n",
+      "Epoch 96/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5565 - acc: 0.7149\n",
+      "Epoch 97/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5367 - acc: 0.7312\n",
+      "Epoch 98/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5237 - acc: 0.7352\n",
+      "Epoch 99/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5533 - acc: 0.7128\n",
+      "Epoch 100/200\n",
+      "491/491 [==============================] - 0s 36us/step - loss: 0.5202 - acc: 0.7251\n",
+      "Epoch 101/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5129 - acc: 0.7434\n",
+      "Epoch 102/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5460 - acc: 0.7271\n",
+      "Epoch 103/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5425 - acc: 0.7128\n",
+      "Epoch 104/200\n",
+      "491/491 [==============================] - 0s 50us/step - loss: 0.5453 - acc: 0.7332\n",
+      "Epoch 105/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.5467 - acc: 0.7413\n",
+      "Epoch 106/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5093 - acc: 0.7332\n",
+      "Epoch 107/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5225 - acc: 0.7373\n",
+      "Epoch 108/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.5485 - acc: 0.7393\n",
+      "Epoch 109/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5205 - acc: 0.7475\n",
+      "Epoch 110/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5279 - acc: 0.7271\n",
+      "Epoch 111/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5498 - acc: 0.7189\n",
+      "Epoch 112/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5209 - acc: 0.7556\n",
+      "Epoch 113/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5232 - acc: 0.7475\n",
+      "Epoch 114/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5220 - acc: 0.7312\n",
+      "Epoch 115/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5749 - acc: 0.7230\n",
+      "Epoch 116/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5229 - acc: 0.7413\n",
+      "Epoch 117/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5282 - acc: 0.7495\n",
+      "Epoch 118/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5175 - acc: 0.7475\n",
+      "Epoch 119/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5270 - acc: 0.7475\n",
+      "Epoch 120/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5235 - acc: 0.7454\n",
+      "Epoch 121/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5012 - acc: 0.7556\n",
+      "Epoch 122/200\n",
+      "491/491 [==============================] - 0s 63us/step - loss: 0.5120 - acc: 0.7393\n",
+      "Epoch 123/200\n",
+      "491/491 [==============================] - 0s 57us/step - loss: 0.5109 - acc: 0.7515\n",
+      "Epoch 124/200\n",
+      "491/491 [==============================] - 0s 50us/step - loss: 0.5148 - acc: 0.7597\n",
+      "Epoch 125/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5197 - acc: 0.7454\n",
+      "Epoch 126/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5219 - acc: 0.7332\n",
+      "Epoch 127/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5100 - acc: 0.7454\n",
+      "Epoch 128/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.5198 - acc: 0.7536\n",
+      "Epoch 129/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5853 - acc: 0.7108\n",
+      "Epoch 130/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5451 - acc: 0.7189\n",
+      "Epoch 131/200\n",
+      "491/491 [==============================] - 0s 44us/step - loss: 0.5253 - acc: 0.7597\n",
+      "Epoch 132/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5158 - acc: 0.7434\n",
+      "Epoch 133/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5499 - acc: 0.7271\n",
+      "Epoch 134/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5832 - acc: 0.7251\n",
+      "Epoch 135/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5473 - acc: 0.7352\n",
+      "Epoch 136/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.5534 - acc: 0.7312\n",
+      "Epoch 137/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5734 - acc: 0.7312\n",
+      "Epoch 138/200\n",
+      "491/491 [==============================] - 0s 44us/step - loss: 0.5463 - acc: 0.7454\n",
+      "Epoch 139/200\n",
+      "491/491 [==============================] - 0s 52us/step - loss: 0.5231 - acc: 0.7658\n",
+      "Epoch 140/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5143 - acc: 0.7556\n",
+      "Epoch 141/200\n",
+      "491/491 [==============================] - 0s 53us/step - loss: 0.5175 - acc: 0.7373\n",
+      "Epoch 142/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5042 - acc: 0.7576\n",
+      "Epoch 143/200\n",
+      "491/491 [==============================] - 0s 52us/step - loss: 0.5536 - acc: 0.7189\n",
+      "Epoch 144/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5442 - acc: 0.7373\n",
+      "Epoch 145/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5598 - acc: 0.7149\n",
+      "Epoch 146/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5208 - acc: 0.7352\n",
+      "Epoch 147/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5155 - acc: 0.7393\n",
+      "Epoch 148/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5261 - acc: 0.7454\n",
+      "Epoch 149/200\n",
+      "491/491 [==============================] - 0s 45us/step - loss: 0.5056 - acc: 0.7475\n",
+      "Epoch 150/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5065 - acc: 0.7352\n",
+      "Epoch 151/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5398 - acc: 0.7454\n",
+      "Epoch 152/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5307 - acc: 0.7271\n",
+      "Epoch 153/200\n",
+      "491/491 [==============================] - 0s 46us/step - loss: 0.5619 - acc: 0.7230\n",
+      "Epoch 154/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5458 - acc: 0.7169\n",
+      "Epoch 155/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5277 - acc: 0.7475\n",
+      "Epoch 156/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5178 - acc: 0.7291\n",
+      "Epoch 157/200\n",
+      "491/491 [==============================] - 0s 61us/step - loss: 0.5588 - acc: 0.7169\n",
+      "Epoch 158/200\n",
+      "491/491 [==============================] - 0s 58us/step - loss: 0.5580 - acc: 0.7108\n",
+      "Epoch 159/200\n",
+      "491/491 [==============================] - 0s 58us/step - loss: 0.5511 - acc: 0.7413\n",
+      "Epoch 160/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5219 - acc: 0.7251\n",
+      "Epoch 161/200\n",
+      "491/491 [==============================] - 0s 42us/step - loss: 0.5107 - acc: 0.7475\n",
+      "Epoch 162/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5265 - acc: 0.7352\n",
+      "Epoch 163/200\n",
+      "491/491 [==============================] - ETA: 0s - loss: 0.5990 - acc: 0.687 - 0s 43us/step - loss: 0.5418 - acc: 0.7413\n",
+      "Epoch 164/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.4966 - acc: 0.7678\n",
+      "Epoch 165/200\n",
+      "491/491 [==============================] - 0s 38us/step - loss: 0.4875 - acc: 0.7536\n",
+      "Epoch 166/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5298 - acc: 0.7149\n",
+      "Epoch 167/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5172 - acc: 0.7413\n",
+      "Epoch 168/200\n",
+      "491/491 [==============================] - ETA: 0s - loss: 0.5668 - acc: 0.718 - 0s 45us/step - loss: 0.5097 - acc: 0.7495\n",
+      "Epoch 169/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5173 - acc: 0.7454\n",
+      "Epoch 170/200\n",
+      "491/491 [==============================] - 0s 49us/step - loss: 0.5037 - acc: 0.7515\n",
+      "Epoch 171/200\n",
+      "491/491 [==============================] - 0s 44us/step - loss: 0.4968 - acc: 0.7576\n",
+      "Epoch 172/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.4993 - acc: 0.7597\n",
+      "Epoch 173/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5585 - acc: 0.7189\n",
+      "Epoch 174/200\n",
+      "491/491 [==============================] - 0s 49us/step - loss: 0.5081 - acc: 0.7719\n",
+      "Epoch 175/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.4881 - acc: 0.7536\n",
+      "Epoch 176/200\n",
+      "491/491 [==============================] - 0s 51us/step - loss: 0.5062 - acc: 0.7454\n",
+      "Epoch 177/200\n",
+      "491/491 [==============================] - 0s 39us/step - loss: 0.5032 - acc: 0.7475\n",
+      "Epoch 178/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.5174 - acc: 0.7434\n",
+      "Epoch 179/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5009 - acc: 0.7556\n",
+      "Epoch 180/200\n",
+      "491/491 [==============================] - 0s 48us/step - loss: 0.4963 - acc: 0.7637\n",
+      "Epoch 181/200\n",
+      "491/491 [==============================] - 0s 44us/step - loss: 0.5959 - acc: 0.7169\n",
+      "Epoch 182/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5459 - acc: 0.7332\n",
+      "Epoch 183/200\n",
+      "491/491 [==============================] - 0s 46us/step - loss: 0.4807 - acc: 0.7658\n",
+      "Epoch 184/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.4977 - acc: 0.7617\n",
+      "Epoch 185/200\n",
+      "491/491 [==============================] - 0s 44us/step - loss: 0.5208 - acc: 0.7413\n",
+      "Epoch 186/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.5005 - acc: 0.7719\n",
+      "Epoch 187/200\n",
+      "491/491 [==============================] - 0s 71us/step - loss: 0.5070 - acc: 0.7536\n",
+      "Epoch 188/200\n",
+      "491/491 [==============================] - 0s 62us/step - loss: 0.5118 - acc: 0.7413\n",
+      "Epoch 189/200\n",
+      "491/491 [==============================] - 0s 49us/step - loss: 0.4859 - acc: 0.7576\n",
+      "Epoch 190/200\n",
+      "491/491 [==============================] - 0s 47us/step - loss: 0.5280 - acc: 0.7393\n",
+      "Epoch 191/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.4907 - acc: 0.7576\n",
+      "Epoch 192/200\n",
+      "491/491 [==============================] - 0s 61us/step - loss: 0.4972 - acc: 0.7699\n",
+      "Epoch 193/200\n",
+      "491/491 [==============================] - 0s 37us/step - loss: 0.5307 - acc: 0.7332\n",
+      "Epoch 194/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.5106 - acc: 0.7536\n",
+      "Epoch 195/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.4805 - acc: 0.7556\n",
+      "Epoch 196/200\n",
+      "491/491 [==============================] - 0s 40us/step - loss: 0.5021 - acc: 0.7536\n",
+      "Epoch 197/200\n",
+      "491/491 [==============================] - 0s 38us/step - loss: 0.4844 - acc: 0.7637\n",
+      "Epoch 198/200\n",
+      "491/491 [==============================] - 0s 35us/step - loss: 0.5113 - acc: 0.7352\n",
+      "Epoch 199/200\n",
+      "491/491 [==============================] - 0s 41us/step - loss: 0.4914 - acc: 0.7536\n",
+      "Epoch 200/200\n",
+      "491/491 [==============================] - 0s 43us/step - loss: 0.4870 - acc: 0.7637\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1aede4036a0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, y_train, epochs=200)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "491/491 [==============================] - 0s 92us/step\n",
+      "Training Accuracy: 74.54%\n",
+      "\n",
+      "154/154 [==============================] - 0s 26us/step\n",
+      "Test Accuracy: 69.48%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "scores = model.evaluate(X_train, y_train)\n",
+    "print(\"Training Accuracy: %.2f%%\\n\" % (scores[1]*100))\n",
+    "scores = model.evaluate(X_test, y_test)\n",
+    "print(\"Test Accuracy: %.2f%%\\n\" % (scores[1]*100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(33.0, 0.5, 'Actual')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_test_pred = model.predict_classes(X_test)\n",
+    "c_matrix = confusion_matrix(y_test, y_test_pred)\n",
+    "ax = sns.heatmap(c_matrix, annot=True,\n",
+    "                 xticklabels=['No diabets', 'Diabets'],\n",
+    "                 yticklabels=['No diabets', 'Diabets'],\n",
+    "                 cbar=False, cmap='Blues')\n",
+    "ax.set_xlabel(\"Prediction\")\n",
+    "ax.set_ylabel(\"Actual\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'True Positive Rate')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_test_pred_probs = model.predict(X_test)\n",
+    "FPR, TPR, _ = roc_curve(y_test, y_test_pred_probs)\n",
+    "plt.plot(FPR, TPR)\n",
+    "plt.plot([0,1],[0,1],'--', color='black') #diagonal line\n",
+    "plt.title('ROC Curve')\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')"
+   ]
+  }
+ ],
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