diff --git a/Projects-ML/Reg-models/Supervised-ML-project.ipynb b/Projects-ML/Reg-models/Supervised-ML-project.ipynb
index 1ef5cef..45d6ef7 100644
--- a/Projects-ML/Reg-models/Supervised-ML-project.ipynb
+++ b/Projects-ML/Reg-models/Supervised-ML-project.ipynb
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -994,12 +994,575 @@
     "print(f'Lasso when large number of alpha {lasso_regressor1.best_score_}')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Now let's use train test method:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "from sklearn.exceptions import ConvergenceWarning\n",
+    "\n",
+    "# Suppress convergence warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear regression = -25.18787473928514\n",
+      "Ridge regression = {'alpha': 0.01}\n",
+      "Ridge regression = -25.18689936738697\n",
+      "Lasso regression = {'alpha': 1e-15}\n",
+      "Lasso regression = -25.18787473928503\n"
+     ]
+    }
+   ],
+   "source": [
+    "lin_rag2 = LinearRegression() \n",
+    "mse = cross_val_score(lin_rag, X_train,y_train,scoring='neg_mean_squared_error', cv=5)\n",
+    "mean_mse = np.mean(mse)\n",
+    "print(f'Linear regression = {mean_mse}')\n",
+    "\n",
+    "ridge2 =Ridge()\n",
+    "params = {'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,35,40,50,55,60,80,90,100]}\n",
+    "ridge_regressor2 = GridSearchCV(ridge2, params, scoring='neg_mean_squared_error', cv=5)\n",
+    "ridge_regressor2.fit(X_train,y_train)\n",
+    "\n",
+    "print(f'Ridge regression = {ridge_regressor2.best_params_}')\n",
+    "print(f'Ridge regression = {ridge_regressor2.best_score_}')\n",
+    "\n",
+    "lasso2 =Lasso()\n",
+    "params = {'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,35,40,50,55,60,80,90,100]}\n",
+    "lasso_regressor2 = GridSearchCV(lasso2, params, scoring='neg_mean_squared_error', cv=5)\n",
+    "lasso_regressor2.fit(X_train,y_train)\n",
+    "\n",
+    "print(f'Lasso regression = {lasso_regressor2.best_params_}')\n",
+    "print(f'Lasso regression = {lasso_regressor2.best_score_}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6832260852266521\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = lin_rag.predict(X_test)\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "r2_score_lin = r2_score(y_pred, y_test)\n",
+    "\n",
+    "print(r2_score_lin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6708743257533069\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = ridge_regressor2.predict(X_test)\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "r2_score_ridge = r2_score(y_pred, y_test)\n",
+    "\n",
+    "print(r2_score_ridge)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.670955897674443\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = lasso_regressor2.predict(X_test)\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "r2_score_lasso = r2_score(y_pred, y_test)\n",
+    "\n",
+    "print(r2_score_lasso)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **Logistic regression**\n",
+    "\n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
+    "\n",
+    "`class sklearn.linear_model.LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression \n",
+    "from sklearn.datasets import load_breast_cancer\n",
+    "\n",
+    "df = load_breast_cancer()\n",
+    "\n",
+    "# Independent features\n",
+    "X = pd.DataFrame(df['data'], columns=df['feature_names'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>mean radius</th>\n",
+       "      <th>mean texture</th>\n",
+       "      <th>mean perimeter</th>\n",
+       "      <th>mean area</th>\n",
+       "      <th>mean smoothness</th>\n",
+       "      <th>mean compactness</th>\n",
+       "      <th>mean concavity</th>\n",
+       "      <th>mean concave points</th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>mean fractal dimension</th>\n",
+       "      <th>...</th>\n",
+       "      <th>worst radius</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.3001</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>0.2419</td>\n",
+       "      <td>0.07871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.38</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.1622</td>\n",
+       "      <td>0.6656</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.0869</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>0.1812</td>\n",
+       "      <td>0.05667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.99</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.1238</td>\n",
+       "      <td>0.1866</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.1974</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>0.2069</td>\n",
+       "      <td>0.05999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.57</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.1444</td>\n",
+       "      <td>0.4245</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.2414</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>0.2597</td>\n",
+       "      <td>0.09744</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.91</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.2098</td>\n",
+       "      <td>0.8663</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.1980</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>0.1809</td>\n",
+       "      <td>0.05883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.54</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.1374</td>\n",
+       "      <td>0.2050</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
+       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
+       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
+       "3        11.42         20.38           77.58      386.1          0.14250   \n",
+       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
+       "\n",
+       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+       "0           0.27760          0.3001              0.14710         0.2419   \n",
+       "1           0.07864          0.0869              0.07017         0.1812   \n",
+       "2           0.15990          0.1974              0.12790         0.2069   \n",
+       "3           0.28390          0.2414              0.10520         0.2597   \n",
+       "4           0.13280          0.1980              0.10430         0.1809   \n",
+       "\n",
+       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
+       "0                 0.07871  ...         25.38          17.33           184.60   \n",
+       "1                 0.05667  ...         24.99          23.41           158.80   \n",
+       "2                 0.05999  ...         23.57          25.53           152.50   \n",
+       "3                 0.09744  ...         14.91          26.50            98.87   \n",
+       "4                 0.05883  ...         22.54          16.67           152.20   \n",
+       "\n",
+       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
+       "0      2019.0            0.1622             0.6656           0.7119   \n",
+       "1      1956.0            0.1238             0.1866           0.2416   \n",
+       "2      1709.0            0.1444             0.4245           0.4504   \n",
+       "3       567.7            0.2098             0.8663           0.6869   \n",
+       "4      1575.0            0.1374             0.2050           0.4000   \n",
+       "\n",
+       "   worst concave points  worst symmetry  worst fractal dimension  \n",
+       "0                0.2654          0.4601                  0.11890  \n",
+       "1                0.1860          0.2750                  0.08902  \n",
+       "2                0.2430          0.3613                  0.08758  \n",
+       "3                0.2575          0.6638                  0.17300  \n",
+       "4                0.1625          0.2364                  0.07678  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Depednent feature \n",
+    "y = pd.DataFrame(df['target'], columns=[\"Target\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Target\n",
+       "0         0\n",
+       "1         0\n",
+       "2         0\n",
+       "3         0\n",
+       "4         0\n",
+       "..      ...\n",
+       "564       0\n",
+       "565       0\n",
+       "566       0\n",
+       "567       0\n",
+       "568       1\n",
+       "\n",
+       "[569 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Target\n",
+       "1    357\n",
+       "0    212\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets check the dataset is imbalanced \n",
+    "y['Target'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "probably it is a balanced dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = [{'C': [1,5,10]}, {'max_iter': [100,150]}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model1 = LogisticRegression(C=100, max_iter=100)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "GridSearchCV(model1, param_grid=params, scoring='f1')"
+   ]
   }
  ],
  "metadata": {