diff --git "a/sk_1_0.ipynb" "b/sk_1_0.ipynb"
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+++ "b/sk_1_0.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import important libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Visualization\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "#Dataset\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "#For Model building and evaluation\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import f1_score, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "#Model saving\n",
+    "import pickle"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dataset Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['sepal length (cm)',\n",
+       " 'sepal width (cm)',\n",
+       " 'petal length (cm)',\n",
+       " 'petal width (cm)']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Loading the Iris data \n",
+    "dataset = load_iris()\n",
+    "\n",
+    "#Feature names\n",
+    "dataset.feature_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Target classes\n",
+    "dataset.target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Target variable where the three values map to the target classes\n",
+    "dataset.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "1                4.9               3.0                1.4               0.2   \n",
+       "2                4.7               3.2                1.3               0.2   \n",
+       "3                4.6               3.1                1.5               0.2   \n",
+       "4                5.0               3.6                1.4               0.2   \n",
+       "\n",
+       "   species  \n",
+       "0        0  \n",
+       "1        0  \n",
+       "2        0  \n",
+       "3        0  \n",
+       "4        0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Creating a dataframe with the features and the target variable\n",
+    "iris_data = pd.DataFrame(\n",
+    "        dataset.data,\n",
+    "        columns=dataset.feature_names,\n",
+    ")\n",
+    "\n",
+    "#Adding the target column\n",
+    "iris_data[\"species\"] = dataset.target\n",
+    "\n",
+    "iris_data.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(150, 5)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal length (cm)    0\n",
+       "sepal width (cm)     0\n",
+       "petal length (cm)    0\n",
+       "petal width (cm)     0\n",
+       "species              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking for null values\n",
+    "iris_data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal length (cm)    float64\n",
+       "sepal width (cm)     float64\n",
+       "petal length (cm)    float64\n",
+       "petal width (cm)     float64\n",
+       "species                int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>3.057333</td>\n",
+       "      <td>3.758000</td>\n",
+       "      <td>1.199333</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>0.435866</td>\n",
+       "      <td>1.765298</td>\n",
+       "      <td>0.762238</td>\n",
+       "      <td>0.819232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.350000</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
+       "count         150.000000        150.000000         150.000000   \n",
+       "mean            5.843333          3.057333           3.758000   \n",
+       "std             0.828066          0.435866           1.765298   \n",
+       "min             4.300000          2.000000           1.000000   \n",
+       "25%             5.100000          2.800000           1.600000   \n",
+       "50%             5.800000          3.000000           4.350000   \n",
+       "75%             6.400000          3.300000           5.100000   \n",
+       "max             7.900000          4.400000           6.900000   \n",
+       "\n",
+       "       petal width (cm)     species  \n",
+       "count        150.000000  150.000000  \n",
+       "mean           1.199333    1.000000  \n",
+       "std            0.762238    0.819232  \n",
+       "min            0.100000    0.000000  \n",
+       "25%            0.300000    0.000000  \n",
+       "50%            1.300000    1.000000  \n",
+       "75%            1.800000    2.000000  \n",
+       "max            2.500000    2.000000  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Description of the data\n",
+    "iris_data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Length and width distribution of each feature\n",
+    "iris_data.hist(edgecolor='black', linewidth=1.2, figsize=(12,6))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model building - Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualizing the correlation of the features using heatmap\n",
+    "plt.figure(figsize=(7,4)) \n",
+    "sns.heatmap(iris_data.corr(),annot=True, cmap='gist_earth_r')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Petal length and width are highly correlated, while sepal length and width are not correlated**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((105, 4), (45, 4))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Splitting into train and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    iris_data.drop('species', axis=1), #predictors\n",
+    "    iris_data['species'], #target\n",
+    "    test_size=0.3, #percentage of observations in test set\n",
+    "    random_state=42 #seed to ensure reproducibility\n",
+    ")\n",
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LogisticRegression()\n",
+    "\n",
+    "#Fitting the model to the train set\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,\n",
+       "       0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0,\n",
+       "       0])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Trained model prediction on the test set\n",
+    "y_pred = model.predict(X_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of the Logistic Regression model: 1.0\n",
+      "\n",
+      "Macro-Averaged F1 score of the Logistic Regression model:  1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Model performance evaluation\n",
+    "print(\"Accuracy of the Logistic Regression model:\", accuracy_score(y_test, y_pred))\n",
+    "print()\n",
+    "print(\"Macro-Averaged F1 score of the Logistic Regression model: \", f1_score(y_test, y_pred, average = 'macro'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Target classes\n",
+    "labels = model.classes_\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "#Confusion matrix visualization for the classifier\n",
+    "cm = confusion_matrix(y_test, y_pred, labels=labels)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
+    "disp.plot(ax=ax, cmap=plt.cm.Blues)\n",
+    "plt.grid(visible=False)\n",
+    "plt.title(\"Confusion Matrix Plot\", size=12)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model saving with Pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filePath = \"logreg_1_0_model.pkl\"\n",
+    "\n",
+    "# Save Model\n",
+    "with open(filePath, \"wb\") as file:\n",
+    "    pickle.dump(model, file)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Proof that it loads on the scikit-learn version 1.0 enivornment**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load Model from version 1.0\n",
+    "filePath = \"logreg_1_0_model.pkl\"\n",
+    "\n",
+    "with open(filePath, \"rb\") as file:\n",
+    "    logreg_model = pickle.load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.81229118, 0.05796635, 0.00209419, 0.77323377, 0.75129815,\n",
+       "       0.04990734, 0.90413955, 0.14749245, 0.78060899, 0.94080708,\n",
+       "       0.2253226 , 0.0372637 , 0.03079458, 0.04394049, 0.02345536,\n",
+       "       0.66333352, 0.02672914, 0.94515067, 0.82008859, 0.04145997,\n",
+       "       0.0412557 , 0.38668089, 0.04369634, 0.05211075, 0.02037042,\n",
+       "       0.10553933, 0.06985485, 0.01949445, 0.03900289, 0.0504813 ,\n",
+       "       0.00679586, 0.01928888, 0.89002387, 0.03531611, 0.01739077,\n",
+       "       0.28537815, 0.81215666, 0.03583244, 0.02188042, 0.01691754,\n",
+       "       0.19405391, 0.73698553, 0.75170193, 0.0182297 , 0.03334529])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred_proba = logreg_model.predict_proba(X_test)[::, 1]\n",
+    "y_pred_proba\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,\n",
+       "       0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0,\n",
+       "       0])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = logreg_model.predict(X_test)\n",
+    "y_pred"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "sklearn_1_0",
+   "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": "3.10.2"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "63d8fd804778431d1f816ad18b512e9a319e37098850ee3505b60af7c43606ce"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}