diff --git a/your-code/bonus.ipynb b/your-code/bonus.ipynb
index 92e4a72..033ebcb 100644
--- a/your-code/bonus.ipynb
+++ b/your-code/bonus.ipynb
@@ -144,9 +144,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/your-code/challenge-1.ipynb b/your-code/challenge-1.ipynb
index 2487c5f..db7cf6c 100644
--- a/your-code/challenge-1.ipynb
+++ b/your-code/challenge-1.ipynb
@@ -34,11 +34,512 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here\n",
+    "df = pd.read_csv('tic-tac-toe.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TL       object\n",
+       "TM       object\n",
+       "TR       object\n",
+       "ML       object\n",
+       "MM       object\n",
+       "MR       object\n",
+       "BL       object\n",
+       "BM       object\n",
+       "BR       object\n",
+       "class      bool\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "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",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TL</th>\n",
+       "      <th>TM</th>\n",
+       "      <th>TR</th>\n",
+       "      <th>ML</th>\n",
+       "      <th>MM</th>\n",
+       "      <th>MR</th>\n",
+       "      <th>BL</th>\n",
+       "      <th>BM</th>\n",
+       "      <th>BR</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>x</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>x</td>\n",
+       "      <td>o</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>x</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>b</td>\n",
+       "      <td>b</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>x</td>\n",
+       "      <td>o</td>\n",
+       "      <td>o</td>\n",
+       "      <td>b</td>\n",
+       "      <td>o</td>\n",
+       "      <td>b</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  TL TM TR ML MM MR BL BM BR  class\n",
+       "0  x  x  x  x  o  o  x  o  o   True\n",
+       "1  x  x  x  x  o  o  o  x  o   True\n",
+       "2  x  x  x  x  o  o  o  o  x   True\n",
+       "3  x  x  x  x  o  o  o  b  b   True\n",
+       "4  x  x  x  x  o  o  b  o  b   True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_x = df.drop(columns='class')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_y = df['class']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-7-3a6e1c804fa5>:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_y['target'] =[1 if i is True else 0 for i in df['class']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_y['target'] =[1 if i is True else 0 for i in df['class']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['TL', 'TM', 'TR', 'ML', 'MM', 'MR', 'BL', 'BM', 'BR'], dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_x.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import preprocessing \n",
+    "label = preprocessing.LabelEncoder()\n",
+    "\n",
+    "# Assigning numerical values and storing in another column\n",
+    "for x in df_x.columns:\n",
+    "    label.fit(df_x[x])\n",
+    "    df_x[x] = label.transform(df_x[x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "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",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TL</th>\n",
+       "      <th>TM</th>\n",
+       "      <th>TR</th>\n",
+       "      <th>ML</th>\n",
+       "      <th>MM</th>\n",
+       "      <th>MR</th>\n",
+       "      <th>BL</th>\n",
+       "      <th>BM</th>\n",
+       "      <th>BR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>953</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>954</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>955</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>956</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>957</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>958 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     TL  TM  TR  ML  MM  MR  BL  BM  BR\n",
+       "0     2   2   2   2   1   1   2   1   1\n",
+       "1     2   2   2   2   1   1   1   2   1\n",
+       "2     2   2   2   2   1   1   1   1   2\n",
+       "3     2   2   2   2   1   1   1   0   0\n",
+       "4     2   2   2   2   1   1   0   1   0\n",
+       "..   ..  ..  ..  ..  ..  ..  ..  ..  ..\n",
+       "953   1   2   2   2   1   1   1   2   2\n",
+       "954   1   2   1   2   2   1   2   1   2\n",
+       "955   1   2   1   2   1   2   2   1   2\n",
+       "956   1   2   1   1   2   2   2   1   2\n",
+       "957   1   1   2   2   2   1   1   2   2\n",
+       "\n",
+       "[958 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_y = df_y['target']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "df_x2 = scaler.fit_transform(df_x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.00322257,  1.08495342,  1.00322257, ...,  1.00322257,\n",
+       "        -0.16731812, -0.28682739],\n",
+       "       [ 1.00322257,  1.08495342,  1.00322257, ..., -0.28682739,\n",
+       "         1.08495342, -0.28682739],\n",
+       "       [ 1.00322257,  1.08495342,  1.00322257, ..., -0.28682739,\n",
+       "        -0.16731812,  1.00322257],\n",
+       "       ...,\n",
+       "       [-0.28682739,  1.08495342, -0.28682739, ...,  1.00322257,\n",
+       "        -0.16731812,  1.00322257],\n",
+       "       [-0.28682739,  1.08495342, -0.28682739, ...,  1.00322257,\n",
+       "        -0.16731812,  1.00322257],\n",
+       "       [-0.28682739, -0.16731812,  1.00322257, ..., -0.28682739,\n",
+       "         1.08495342,  1.00322257]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_x2"
    ]
   },
   {
@@ -60,11 +561,881 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df_x2, df_y, test_size =0.2, random_state=0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
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+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
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+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 0,\n",
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+       " 1,\n",
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+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
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+       " 1,\n",
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+       " 1,\n",
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+       " 0,\n",
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+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
+       " 0,\n",
+       " 1,\n",
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+       " 0,\n",
+       " 0,\n",
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+       " 1,\n",
+       " 0,\n",
+       " 1,\n",
+       " 1,\n",
+       " 1,\n",
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+       " 0]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.exceptions import ConvergenceWarning\n",
+    "\n",
+    "# MLP classifiers is a deep learning method that relies on an underlying Neural Network to perform the task of classification.\n",
+    "\n",
+    "from sklearn.neural_network import MLPClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modeltic = MLPClassifier(hidden_layer_sizes= (40,80,200) ,max_iter=200, alpha=1e-4,\n",
+    "                    solver='sgd', verbose=10, random_state=1,\n",
+    "                    learning_rate_init=.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1, loss = 0.68769328\n",
+      "Iteration 2, loss = 0.60252996\n",
+      "Iteration 3, loss = 0.57637062\n",
+      "Iteration 4, loss = 0.54134144\n",
+      "Iteration 5, loss = 0.48215865\n",
+      "Iteration 6, loss = 0.67715806\n",
+      "Iteration 7, loss = 0.52238327\n",
+      "Iteration 8, loss = 0.43673264\n",
+      "Iteration 9, loss = 0.33184606\n",
+      "Iteration 10, loss = 0.20857894\n",
+      "Iteration 11, loss = 0.77441583\n",
+      "Iteration 12, loss = 0.27238008\n",
+      "Iteration 13, loss = 0.17069597\n",
+      "Iteration 14, loss = 0.09593033\n",
+      "Iteration 15, loss = 0.04732005\n",
+      "Iteration 16, loss = 0.02361511\n",
+      "Iteration 17, loss = 0.01250102\n",
+      "Iteration 18, loss = 0.00619385\n",
+      "Iteration 19, loss = 0.00341516\n",
+      "Iteration 20, loss = 0.00213685\n",
+      "Iteration 21, loss = 0.00147934\n",
+      "Iteration 22, loss = 0.00111550\n",
+      "Iteration 23, loss = 0.00088696\n",
+      "Iteration 24, loss = 0.00073755\n",
+      "Iteration 25, loss = 0.00064388\n",
+      "Iteration 26, loss = 0.00057676\n",
+      "Iteration 27, loss = 0.00052795\n",
+      "Iteration 28, loss = 0.00048895\n",
+      "Iteration 29, loss = 0.00046057\n",
+      "Iteration 30, loss = 0.00043602\n",
+      "Iteration 31, loss = 0.00041532\n",
+      "Iteration 32, loss = 0.00039874\n",
+      "Iteration 33, loss = 0.00038320\n",
+      "Iteration 34, loss = 0.00036979\n",
+      "Iteration 35, loss = 0.00035788\n",
+      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
+      "Training set score: 1.000000\n",
+      "Test set score: 0.994792\n"
+     ]
+    }
+   ],
+   "source": [
+    "modeltic = modeltic.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Training set score: %f\" % modeltic.score(X_train, y_train))\n",
+    "print(\"Test set score: %f\" % modeltic.score(X_test, y_test))"
    ]
   },
   {
@@ -78,11 +1449,176 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "predictions = modeltic.predict(df_x2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = pd.DataFrame(predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>956</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>957</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>958 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0\n",
+       "0    1\n",
+       "1    1\n",
+       "2    1\n",
+       "3    1\n",
+       "4    1\n",
+       "..  ..\n",
+       "953  0\n",
+       "954  0\n",
+       "955  0\n",
+       "956  0\n",
+       "957  0\n",
+       "\n",
+       "[958 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_y = pd.DataFrame(df_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions['check']=df_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions['check2'] = predictions[0] == predictions['check']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True     957\n",
+       "False      1\n",
+       "Name: check2, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions['check2'].value_counts()"
    ]
   },
   {
@@ -104,13 +1640,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
     "# your code here"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'keras'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-73b860e02b28>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mkeras\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_categorical\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'keras'"
+     ]
+    }
+   ],
+   "source": [
+    "import keras as ks\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt = df_x2.keras.optimizers.Adam(learning_rate=0.1)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -144,9 +1713,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }