diff --git a/MidEval Code/MidEval_DoddiGunaVenkat/DoddiGunaVenkat_MidEval.ipynb b/MidEval Code/MidEval_DoddiGunaVenkat/DoddiGunaVenkat_MidEval.ipynb
new file mode 100644
index 0000000..3492818
--- /dev/null
+++ b/MidEval Code/MidEval_DoddiGunaVenkat/DoddiGunaVenkat_MidEval.ipynb	
@@ -0,0 +1,2573 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7125e9e1",
+   "metadata": {},
+   "source": [
+    "# Load necessary libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "444f6c6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Preprocessing\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "\n",
+    "\n",
+    "# Models\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix,accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, precision_recall_curve, average_precision_score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "30fd5fdc",
+   "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>lookback_days</th>\n",
+       "      <th>asset_type</th>\n",
+       "      <th>market_regime</th>\n",
+       "      <th>high_volatility</th>\n",
+       "      <th>trend_continuation</th>\n",
+       "      <th>technical_score</th>\n",
+       "      <th>edge_density</th>\n",
+       "      <th>slope_strength</th>\n",
+       "      <th>candlestick_variance</th>\n",
+       "      <th>pattern_symmetry</th>\n",
+       "      <th>future_trend</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>equity</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>59.99</td>\n",
+       "      <td>0.504</td>\n",
+       "      <td>0.298</td>\n",
+       "      <td>1.572</td>\n",
+       "      <td>0.768</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38</td>\n",
+       "      <td>index</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>78.54</td>\n",
+       "      <td>0.559</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>0.692</td>\n",
+       "      <td>0.538</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24</td>\n",
+       "      <td>equity</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.03</td>\n",
+       "      <td>0.617</td>\n",
+       "      <td>0.212</td>\n",
+       "      <td>1.419</td>\n",
+       "      <td>0.301</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>52</td>\n",
+       "      <td>equity</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66.51</td>\n",
+       "      <td>0.360</td>\n",
+       "      <td>0.347</td>\n",
+       "      <td>0.699</td>\n",
+       "      <td>0.498</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17</td>\n",
+       "      <td>equity</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>61.21</td>\n",
+       "      <td>0.492</td>\n",
+       "      <td>0.144</td>\n",
+       "      <td>2.520</td>\n",
+       "      <td>0.828</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   lookback_days asset_type  ... pattern_symmetry  future_trend\n",
+       "0             48     equity  ...            0.768             1\n",
+       "1             38      index  ...            0.538             1\n",
+       "2             24     equity  ...            0.301             1\n",
+       "3             52     equity  ...            0.498             1\n",
+       "4             17     equity  ...            0.828             1\n",
+       "\n",
+       "[5 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('quantvision_financial_dataset_200.csv') # load dataset\n",
+    "\n",
+    "df.head() # first 5 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9475c9e0",
+   "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>lookback_days</th>\n",
+       "      <th>asset_type</th>\n",
+       "      <th>market_regime</th>\n",
+       "      <th>high_volatility</th>\n",
+       "      <th>trend_continuation</th>\n",
+       "      <th>technical_score</th>\n",
+       "      <th>edge_density</th>\n",
+       "      <th>slope_strength</th>\n",
+       "      <th>candlestick_variance</th>\n",
+       "      <th>pattern_symmetry</th>\n",
+       "      <th>future_trend</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>48</td>\n",
+       "      <td>index</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>71.27</td>\n",
+       "      <td>0.515</td>\n",
+       "      <td>-0.285</td>\n",
+       "      <td>1.614</td>\n",
+       "      <td>0.774</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>15</td>\n",
+       "      <td>equity</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39.32</td>\n",
+       "      <td>0.421</td>\n",
+       "      <td>-0.037</td>\n",
+       "      <td>2.009</td>\n",
+       "      <td>0.506</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>17</td>\n",
+       "      <td>index</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.31</td>\n",
+       "      <td>0.624</td>\n",
+       "      <td>-0.629</td>\n",
+       "      <td>2.049</td>\n",
+       "      <td>0.523</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>36</td>\n",
+       "      <td>equity</td>\n",
+       "      <td>bullish</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66.78</td>\n",
+       "      <td>0.523</td>\n",
+       "      <td>-0.628</td>\n",
+       "      <td>1.246</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>18</td>\n",
+       "      <td>index</td>\n",
+       "      <td>bearish</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>59.52</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.828</td>\n",
+       "      <td>2.228</td>\n",
+       "      <td>0.841</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     lookback_days asset_type  ... pattern_symmetry  future_trend\n",
+       "195             48      index  ...            0.774             0\n",
+       "196             15     equity  ...            0.506             1\n",
+       "197             17      index  ...            0.523             0\n",
+       "198             36     equity  ...            1.000             1\n",
+       "199             18      index  ...            0.841             1\n",
+       "\n",
+       "[5 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail() # last 5 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a4bac419",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Continuous columns: Index(['lookback_days', 'high_volatility', 'trend_continuation',\n",
+      "       'technical_score', 'edge_density', 'slope_strength',\n",
+      "       'candlestick_variance', 'pattern_symmetry', 'future_trend'],\n",
+      "      dtype='object')\n",
+      "Categorical columns: Index(['asset_type', 'market_regime'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "continous_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
+    "categorical_columns = df.select_dtypes(include=['object']).columns\n",
+    "\n",
+    "print(\"Continuous columns:\", continous_columns)\n",
+    "print(\"Categorical columns:\", categorical_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e37e59b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 200 entries, 0 to 199\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   lookback_days         200 non-null    int64  \n",
+      " 1   asset_type            200 non-null    object \n",
+      " 2   market_regime         200 non-null    object \n",
+      " 3   high_volatility       200 non-null    int64  \n",
+      " 4   trend_continuation    200 non-null    int64  \n",
+      " 5   technical_score       200 non-null    float64\n",
+      " 6   edge_density          200 non-null    float64\n",
+      " 7   slope_strength        200 non-null    float64\n",
+      " 8   candlestick_variance  200 non-null    float64\n",
+      " 9   pattern_symmetry      200 non-null    float64\n",
+      " 10  future_trend          200 non-null    int64  \n",
+      "dtypes: float64(5), int64(4), object(2)\n",
+      "memory usage: 17.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info() # dataset info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b88a7771",
+   "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>lookback_days</th>\n",
+       "      <th>high_volatility</th>\n",
+       "      <th>trend_continuation</th>\n",
+       "      <th>technical_score</th>\n",
+       "      <th>edge_density</th>\n",
+       "      <th>slope_strength</th>\n",
+       "      <th>candlestick_variance</th>\n",
+       "      <th>pattern_symmetry</th>\n",
+       "      <th>future_trend</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>34.635000</td>\n",
+       "      <td>0.360000</td>\n",
+       "      <td>0.610000</td>\n",
+       "      <td>61.846850</td>\n",
+       "      <td>0.464105</td>\n",
+       "      <td>0.250510</td>\n",
+       "      <td>1.279245</td>\n",
+       "      <td>0.613830</td>\n",
+       "      <td>0.935000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>14.656598</td>\n",
+       "      <td>0.481205</td>\n",
+       "      <td>0.488974</td>\n",
+       "      <td>15.028468</td>\n",
+       "      <td>0.116050</td>\n",
+       "      <td>0.619966</td>\n",
+       "      <td>0.575535</td>\n",
+       "      <td>0.173577</td>\n",
+       "      <td>0.247144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>24.200000</td>\n",
+       "      <td>0.191000</td>\n",
+       "      <td>-1.217000</td>\n",
+       "      <td>0.028000</td>\n",
+       "      <td>0.210000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>51.055000</td>\n",
+       "      <td>0.384000</td>\n",
+       "      <td>-0.174000</td>\n",
+       "      <td>0.838000</td>\n",
+       "      <td>0.483000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>61.570000</td>\n",
+       "      <td>0.444500</td>\n",
+       "      <td>0.273500</td>\n",
+       "      <td>1.264000</td>\n",
+       "      <td>0.601500</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>72.122500</td>\n",
+       "      <td>0.531500</td>\n",
+       "      <td>0.696750</td>\n",
+       "      <td>1.663250</td>\n",
+       "      <td>0.745250</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>0.798000</td>\n",
+       "      <td>1.833000</td>\n",
+       "      <td>2.520000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       lookback_days  high_volatility  ...  pattern_symmetry  future_trend\n",
+       "count     200.000000       200.000000  ...        200.000000    200.000000\n",
+       "mean       34.635000         0.360000  ...          0.613830      0.935000\n",
+       "std        14.656598         0.481205  ...          0.173577      0.247144\n",
+       "min        10.000000         0.000000  ...          0.210000      0.000000\n",
+       "25%        22.000000         0.000000  ...          0.483000      1.000000\n",
+       "50%        35.000000         0.000000  ...          0.601500      1.000000\n",
+       "75%        48.000000         1.000000  ...          0.745250      1.000000\n",
+       "max        60.000000         1.000000  ...          1.000000      1.000000\n",
+       "\n",
+       "[8 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe() # statistical summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7c13eea",
+   "metadata": {},
+   "source": [
+    "### Basic Sanity Check\n",
+    "\n",
+    "- Check for null count\n",
+    "- Check for duplicated rows\n",
+    "- Check for Predictor Variable (Trend Continution) distibution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a366cc53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lookback_days           0\n",
+      "asset_type              0\n",
+      "market_regime           0\n",
+      "high_volatility         0\n",
+      "trend_continuation      0\n",
+      "technical_score         0\n",
+      "edge_density            0\n",
+      "slope_strength          0\n",
+      "candlestick_variance    0\n",
+      "pattern_symmetry        0\n",
+      "future_trend            0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# null counts\n",
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b5b78142",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Duplicated rows: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "future_trend\n",
+       "1    0.935\n",
+       "0    0.065\n",
+       "Name: proportion, dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Duplicate rows\n",
+    "print('Duplicated rows:',df.duplicated().sum())\n",
+    "\n",
+    "# Target distribution\n",
+    "df['future_trend'].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "431a51ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='future_trend'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['future_trend'].value_counts().plot(kind='bar') # predictor variable distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d69a029",
+   "metadata": {},
+   "source": [
+    "> **Conclusion:** From sanity check following are observations made\n",
+    "> - _no.of nulls = 0_\n",
+    "> - _no.of duplicates = 0_\n",
+    "> - _predictor variable is skewed_ => dataset is highly imbalanced, since future_trend has proprotions of 0.935 and 0.065 for values 1 and 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ce7b3f5",
+   "metadata": {},
+   "source": [
+    "# 4.1 EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d2d5cf6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# divide features and target\n",
+    "\n",
+    "target = 'future_trend'\n",
+    "\n",
+    "categorical_features = ['asset_type', 'market_regime']\n",
+    "binary_features = ['high_volatility', 'trend_continuation']\n",
+    "numerical_features = [\n",
+    "    'lookback_days',\n",
+    "    'technical_score',\n",
+    "    'edge_density',\n",
+    "    'slope_strength',\n",
+    "    'candlestick_variance',\n",
+    "    'pattern_symmetry'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "521fe8cd",
+   "metadata": {},
+   "source": [
+    "### EDA on numerical_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c7ca4e26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Feature: lookback_days\n",
+      "  1: 0 outliers\n",
+      "  0: 0 outliers\n",
+      "\n",
+      "Feature: technical_score\n",
+      "  1: 0 outliers\n",
+      "  0: 1 outliers\n",
+      "     technical_score\n",
+      "195            71.27\n",
+      "\n",
+      "Feature: edge_density\n",
+      "  1: 2 outliers\n",
+      "     edge_density\n",
+      "102         0.798\n",
+      "104         0.757\n",
+      "  0: 1 outliers\n",
+      "    edge_density\n",
+      "23         0.201\n",
+      "\n",
+      "Feature: slope_strength\n",
+      "  1: 0 outliers\n",
+      "  0: 1 outliers\n",
+      "     slope_strength\n",
+      "168           0.696\n",
+      "\n",
+      "Feature: candlestick_variance\n",
+      "  1: 0 outliers\n",
+      "  0: 0 outliers\n",
+      "\n",
+      "Feature: pattern_symmetry\n",
+      "  1: 0 outliers\n",
+      "  0: 1 outliers\n",
+      "     pattern_symmetry\n",
+      "195             0.774\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, 2, figsize=(12, 10))\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, col in enumerate(numerical_features):\n",
+    "    sns.boxplot(\n",
+    "        x='future_trend',\n",
+    "        y=col,\n",
+    "        data=df,\n",
+    "        ax=axes[i]\n",
+    "    )\n",
+    "    axes[i].set_title(f\"{col} vs Future Trend\")\n",
+    "\n",
+    "    print(f\"\\nFeature: {col}\")\n",
+    "\n",
+    "    for trend in df['future_trend'].unique():\n",
+    "        subset = df[df['future_trend'] == trend][col]\n",
+    "\n",
+    "        Q1 = subset.quantile(0.25)\n",
+    "        Q3 = subset.quantile(0.75)\n",
+    "        IQR = Q3 - Q1\n",
+    "\n",
+    "        lower = Q1 - 1.5 * IQR\n",
+    "        upper = Q3 + 1.5 * IQR\n",
+    "\n",
+    "        outliers = df[\n",
+    "            (df['future_trend'] == trend) &\n",
+    "            ((df[col] < lower) | (df[col] > upper))\n",
+    "        ]\n",
+    "\n",
+    "        print(f\"  {trend}: {len(outliers)} outliers\")\n",
+    "\n",
+    "        if not outliers.empty:\n",
+    "            print(outliers[[col]])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d47634a3",
+   "metadata": {},
+   "source": [
+    "## Numerical Features — Final Analysis\n",
+    "\n",
+    "### Features analyzed\n",
+    "\n",
+    "* `lookback_days`\n",
+    "* `technical_score`\n",
+    "* `edge_density`\n",
+    "* `slope_strength`\n",
+    "* `candlestick_variance`\n",
+    "* `pattern_symmetry`\n",
+    "\n",
+    "### What was done\n",
+    "\n",
+    "* Boxplots were created **grouped by the target (`future_trend`)**\n",
+    "* Outliers were detected using the **IQR method per class**\n",
+    "* This matched seaborn’s boxplot logic (class-wise quartiles)\n",
+    "\n",
+    "### Key observations\n",
+    "\n",
+    "* Most numerical features are **well-behaved**\n",
+    "* Outliers are **rare and class-specific**, not global\n",
+    "* `edge_density` shows the most extreme values across both classes\n",
+    "* Other features have at most **1–2 isolated outliers**\n",
+    "\n",
+    "### Interpretation\n",
+    "\n",
+    "* These outliers appear to be **valid observations**, not data errors\n",
+    "* There is no evidence of systematic contamination or heavy tails\n",
+    "* Global outlier removal would be inappropriate\n",
+    "\n",
+    "### Modeling implications\n",
+    "\n",
+    "* Numerical features are **safe to keep**\n",
+    "* Standard scaling is required\n",
+    "* Regularized logistic regression will naturally limit outlier influence\n",
+    "* No feature should be dropped solely due to outliers\n",
+    "\n",
+    "### Conclusion\n",
+    "\n",
+    "Numerical features are clean, informative, and suitable for logistic regression with scaling and regularization.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fb80e77",
+   "metadata": {},
+   "source": [
+    "### EDA on categorical_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ab3ab928",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Counts for feature: asset_type\n",
+      "future_trend  0   1\n",
+      "asset_type         \n",
+      "crypto        3  38\n",
+      "equity        6  99\n",
+      "index         4  50\n",
+      "\n",
+      "Total per class:\n",
+      "future_trend\n",
+      "0     13\n",
+      "1    187\n",
+      "dtype: int64\n",
+      "\n",
+      "Counts for feature: market_regime\n",
+      "future_trend   0   1\n",
+      "market_regime       \n",
+      "bearish        4  56\n",
+      "bullish        5  78\n",
+      "sideways       4  53\n",
+      "\n",
+      "Total per class:\n",
+      "future_trend\n",
+      "0     13\n",
+      "1    187\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(\n",
+    "    1, len(categorical_features),\n",
+    "    figsize=(6 * len(categorical_features), 4)\n",
+    ")\n",
+    "\n",
+    "# Ensure axes is iterable\n",
+    "if len(categorical_features) == 1:\n",
+    "    axes = [axes]\n",
+    "\n",
+    "for i, col in enumerate(categorical_features):\n",
+    "    # ---- PLOT ----\n",
+    "    sns.countplot(\n",
+    "        x=col,\n",
+    "        hue='future_trend',\n",
+    "        data=df,\n",
+    "        ax=axes[i]\n",
+    "    )\n",
+    "    axes[i].set_title(f\"{col} Distribution by Future Trend\")\n",
+    "    axes[i].set_xlabel(col)\n",
+    "    axes[i].set_ylabel('Count')\n",
+    "    axes[i].legend(title='Future Trend')\n",
+    "\n",
+    "    # ---- PRINT COUNTS ----\n",
+    "    print(f\"\\nCounts for feature: {col}\")\n",
+    "\n",
+    "    # Category × class counts\n",
+    "    ct = pd.crosstab(df[col], df['future_trend'])\n",
+    "    print(ct)\n",
+    "\n",
+    "    # Total counts per class (for this feature)\n",
+    "    print(\"\\nTotal per class:\")\n",
+    "    print(ct.sum())\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c3b2f2f",
+   "metadata": {},
+   "source": [
+    "## Categorical Features — Final Analysis\n",
+    "\n",
+    "### Features analyzed\n",
+    "\n",
+    "* `asset_type`\n",
+    "* `market_regime`\n",
+    "\n",
+    "### What was done\n",
+    "\n",
+    "* Count plots grouped by `future_trend`\n",
+    "* Cross-tabulation to inspect class distributions\n",
+    "\n",
+    "### Key observations\n",
+    "\n",
+    "* All categories appear in **both classes**\n",
+    "* No category perfectly predicts the target\n",
+    "* Distributions across categories are broadly similar\n",
+    "* Class imbalance dominates category counts\n",
+    "\n",
+    "### Critical finding\n",
+    "\n",
+    "* The target variable is **severely imbalanced**\n",
+    "\n",
+    "  * Class `1`: 187 samples\n",
+    "  * Class `0`: 13 samples\n",
+    "\n",
+    "### Interpretation\n",
+    "\n",
+    "* Categorical features themselves are **not problematic**\n",
+    "* The imbalance means their apparent effect may be overstated or understated\n",
+    "* There is no evidence of data leakage or category exclusivity\n",
+    "\n",
+    "### Modeling implications\n",
+    "\n",
+    "* One-hot encoding is appropriate\n",
+    "* Class weighting is required\n",
+    "* Coefficients should be interpreted cautiously\n",
+    "* Accuracy is not a meaningful metric here\n",
+    "\n",
+    "### Conclusion\n",
+    "\n",
+    "Categorical features are structurally sound, but their usefulness depends on handling target imbalance correctly.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97b7e965",
+   "metadata": {},
+   "source": [
+    "### EDA on binary_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bad0533b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Counts for feature: high_volatility\n",
+      "future_trend      0    1\n",
+      "high_volatility         \n",
+      "0                 1  127\n",
+      "1                12   60\n",
+      "\n",
+      "Total per class:\n",
+      "future_trend\n",
+      "0     13\n",
+      "1    187\n",
+      "dtype: int64\n",
+      "\n",
+      "Counts for feature: trend_continuation\n",
+      "future_trend         0    1\n",
+      "trend_continuation         \n",
+      "0                   12   66\n",
+      "1                    1  121\n",
+      "\n",
+      "Total per class:\n",
+      "future_trend\n",
+      "0     13\n",
+      "1    187\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(\n",
+    "    1, len(binary_features),\n",
+    "    figsize=(6 * len(binary_features), 4)\n",
+    ")\n",
+    "\n",
+    "# Ensure axes is iterable\n",
+    "if len(binary_features) == 1:\n",
+    "    axes = [axes]\n",
+    "\n",
+    "for i, col in enumerate(binary_features):\n",
+    "    # ---- PLOT ----\n",
+    "    sns.countplot(\n",
+    "        x=col,\n",
+    "        hue='future_trend',\n",
+    "        data=df,\n",
+    "        ax=axes[i]\n",
+    "    )\n",
+    "    axes[i].set_title(f\"{col} Distribution by Future Trend\")\n",
+    "    axes[i].set_xlabel(col)\n",
+    "    axes[i].set_ylabel('Count')\n",
+    "    axes[i].legend(title='Future Trend')\n",
+    "\n",
+    "    # ---- PRINT COUNTS ----\n",
+    "    print(f\"\\nCounts for feature: {col}\")\n",
+    "\n",
+    "    # Category × class counts\n",
+    "    ct = pd.crosstab(df[col], df['future_trend'])\n",
+    "    print(ct)\n",
+    "\n",
+    "    # Total counts per class (for this feature)\n",
+    "    print(\"\\nTotal per class:\")\n",
+    "    print(ct.sum())\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bea62cd",
+   "metadata": {},
+   "source": [
+    "## Binary Features — Final Analysis\n",
+    "\n",
+    "### Features analyzed\n",
+    "\n",
+    "* `high_volatility`\n",
+    "* `trend_continuation`\n",
+    "\n",
+    "### What was done\n",
+    "\n",
+    "* Cross-tabulated binary values against `future_trend`\n",
+    "* Inspected cell counts for separation risk\n",
+    "\n",
+    "### Key observations\n",
+    "\n",
+    "* Both features show **strong association** with the target\n",
+    "* Minority class observations are concentrated in specific binary states\n",
+    "* Some cells contain **very small counts (1–2 samples)**\n",
+    "\n",
+    "### Interpretation\n",
+    "\n",
+    "* These features are **highly informative**\n",
+    "* However, they introduce **quasi-complete separation risk**\n",
+    "* Coefficients may become unstable without constraints\n",
+    "\n",
+    "### Modeling implications\n",
+    "\n",
+    "* These features should **not be removed**\n",
+    "* Strong regularization is required\n",
+    "* Class-weighted logistic regression is mandatory\n",
+    "* Coefficient magnitudes must be monitored\n",
+    "\n",
+    "### Conclusion\n",
+    "\n",
+    "Binary features carry the strongest signal in the dataset but must be handled with care to avoid overfitting and unstable estimates.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f6d0838",
+   "metadata": {},
+   "source": [
+    "## Overall Summary\n",
+    "\n",
+    "* Numerical features are clean and stable\n",
+    "* Categorical features are valid but overshadowed by class imbalance\n",
+    "* Binary features provide strong signal with separation risk\n",
+    "* The dominant issue is **target imbalance**, not feature quality\n",
+    "\n",
+    "### Recommended modeling setup\n",
+    "\n",
+    "* Standardize numerical features\n",
+    "* One-hot encode categorical features\n",
+    "* Use **class-weighted, regularized logistic regression**\n",
+    "* Evaluate using recall, precision, and ROC-AUC — not accuracy\n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f96c3f72",
+   "metadata": {},
+   "source": [
+    "# Advanced EDA and Pre-Processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8de9d7a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# kde plots for numerical features\n",
+    "\n",
+    "fig, axes = plt.subplots(3, 2, figsize=(12, 10))\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, col in enumerate(numerical_features):\n",
+    "    sns.kdeplot(\n",
+    "        data=df,\n",
+    "        x=col,\n",
+    "        hue='future_trend',\n",
+    "        fill=True,\n",
+    "        common_norm=False,\n",
+    "        alpha=0.4,\n",
+    "        ax=axes[i]\n",
+    "    )\n",
+    "    axes[i].set_title(f\"KDE Distribution of {col} by Future Trend\")\n",
+    "    axes[i].set_xlabel(col)\n",
+    "    axes[i].set_ylabel(\"Density\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e08745d2",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "## KDE Distribution Analysis (Feature vs Target)\n",
+    "\n",
+    "### Lookback Days vs Future Trend\n",
+    "\n",
+    "* KDE plots show **two distinct peaks for both classes (0 and 1)**.\n",
+    "* Indicates **multi-modal behavior**:\n",
+    "\n",
+    "  * Different trading horizons (short-term vs mid-term) influence price movement.\n",
+    "* Heavy overlap between classes → **weak standalone predictor**.\n",
+    "* Suggests **interaction effects** with other features (e.g., regime, volatility).\n",
+    "\n",
+    " **Interpretation**\n",
+    "Lookback_days alone does not strongly separate up/down moves but may be useful when combined with **market regime or trend continuation**.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  Trend Continuation (Binary)\n",
+    "\n",
+    "* Both classes present in both states (0/1).\n",
+    "* Slight skew towards `future_trend = 1` when continuation = 1.\n",
+    "* Not deterministic → **probabilistic influence only**.\n",
+    "\n",
+    "**Interpretation**\n",
+    "Trend continuation increases likelihood of upside but **cannot be used as a rule-based signal**.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  Other Numerical Features (General KDE Behavior)\n",
+    "\n",
+    "* Distributions show:\n",
+    "\n",
+    "  * **Moderate skewness**\n",
+    "  * **Long right tails**\n",
+    "* Heavy overlap across classes.\n",
+    "* No feature exhibits a clean, single-mode separation.\n",
+    "\n",
+    "**Interpretation**\n",
+    "This confirms:\n",
+    "\n",
+    "* Linear separation is weak\n",
+    "* **Non-linear models** (tree-based, boosting) are more suitable than pure linear models.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Distribution Shape Implications (Model Choice)\n",
+    "\n",
+    "| Observation               | Modeling Impact             |\n",
+    "| ------------------------- | --------------------------- |\n",
+    "| Multi-modal distributions | Linear models struggle      |\n",
+    "| Long-tailed features      | Scaling required            |\n",
+    "| Overlapping KDEs          | Need ensemble / boosting    |\n",
+    "| Weak marginal separation  | Feature interactions matter |\n",
+    "\n",
+    "**Decision**\n",
+    "\n",
+    "Use:\n",
+    "\n",
+    "* Gradient Boosting / XGBoost\n",
+    "* Feature interaction-aware models\n",
+    "* Probability-based outputs (not hard rules)\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "796d8e04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pearson Correlation Matrix\n"
+     ]
+    },
+    {
+     "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>lookback_days</th>\n",
+       "      <th>technical_score</th>\n",
+       "      <th>edge_density</th>\n",
+       "      <th>slope_strength</th>\n",
+       "      <th>candlestick_variance</th>\n",
+       "      <th>pattern_symmetry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>lookback_days</th>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>-0.060</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>-0.077</td>\n",
+       "      <td>0.018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technical_score</th>\n",
+       "      <td>0.040</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>-0.161</td>\n",
+       "      <td>0.403</td>\n",
+       "      <td>-0.257</td>\n",
+       "      <td>0.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>edge_density</th>\n",
+       "      <td>-0.060</td>\n",
+       "      <td>-0.161</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>0.415</td>\n",
+       "      <td>-0.023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slope_strength</th>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>0.403</td>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>-0.139</td>\n",
+       "      <td>0.267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>candlestick_variance</th>\n",
+       "      <td>-0.077</td>\n",
+       "      <td>-0.257</td>\n",
+       "      <td>0.415</td>\n",
+       "      <td>-0.139</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>-0.021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pattern_symmetry</th>\n",
+       "      <td>0.018</td>\n",
+       "      <td>0.347</td>\n",
+       "      <td>-0.023</td>\n",
+       "      <td>0.267</td>\n",
+       "      <td>-0.021</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      lookback_days  ...  pattern_symmetry\n",
+       "lookback_days                 1.000  ...             0.018\n",
+       "technical_score               0.040  ...             0.347\n",
+       "edge_density                 -0.060  ...            -0.023\n",
+       "slope_strength               -0.125  ...             0.267\n",
+       "candlestick_variance         -0.077  ...            -0.021\n",
+       "pattern_symmetry              0.018  ...             1.000\n",
+       "\n",
+       "[6 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# pearson correlation matrix\n",
+    "\n",
+    "corr_pearson = df[numerical_features].corr(method='pearson')\n",
+    "\n",
+    "print(\"Pearson Correlation Matrix\")\n",
+    "corr_pearson.round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "565e7b32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Spearman Correlation Matrix\n"
+     ]
+    },
+    {
+     "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>lookback_days</th>\n",
+       "      <th>technical_score</th>\n",
+       "      <th>edge_density</th>\n",
+       "      <th>slope_strength</th>\n",
+       "      <th>candlestick_variance</th>\n",
+       "      <th>pattern_symmetry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>lookback_days</th>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>-0.048</td>\n",
+       "      <td>-0.111</td>\n",
+       "      <td>-0.068</td>\n",
+       "      <td>0.017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technical_score</th>\n",
+       "      <td>0.027</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>-0.150</td>\n",
+       "      <td>0.414</td>\n",
+       "      <td>-0.248</td>\n",
+       "      <td>0.362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>edge_density</th>\n",
+       "      <td>-0.048</td>\n",
+       "      <td>-0.150</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>-0.115</td>\n",
+       "      <td>0.426</td>\n",
+       "      <td>-0.024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>slope_strength</th>\n",
+       "      <td>-0.111</td>\n",
+       "      <td>0.414</td>\n",
+       "      <td>-0.115</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>-0.151</td>\n",
+       "      <td>0.245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>candlestick_variance</th>\n",
+       "      <td>-0.068</td>\n",
+       "      <td>-0.248</td>\n",
+       "      <td>0.426</td>\n",
+       "      <td>-0.151</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>-0.029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pattern_symmetry</th>\n",
+       "      <td>0.017</td>\n",
+       "      <td>0.362</td>\n",
+       "      <td>-0.024</td>\n",
+       "      <td>0.245</td>\n",
+       "      <td>-0.029</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      lookback_days  ...  pattern_symmetry\n",
+       "lookback_days                 1.000  ...             0.017\n",
+       "technical_score               0.027  ...             0.362\n",
+       "edge_density                 -0.048  ...            -0.024\n",
+       "slope_strength               -0.111  ...             0.245\n",
+       "candlestick_variance         -0.068  ...            -0.029\n",
+       "pattern_symmetry              0.017  ...             1.000\n",
+       "\n",
+       "[6 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# spearman correlation\n",
+    "\n",
+    "corr_spearman = df[numerical_features].corr(method='spearman')\n",
+    "\n",
+    "print(\"\\nSpearman Correlation Matrix\")\n",
+    "corr_spearman.round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "818bf519",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(\n",
+    "    corr_spearman,\n",
+    "    annot=True,\n",
+    "    fmt=\".2f\",\n",
+    "    cmap=\"coolwarm\",\n",
+    "    center=0,\n",
+    "    linewidths=0.5\n",
+    ")\n",
+    "plt.title(\"Spearman Correlation Heatmap (Numerical Features)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcc9921b",
+   "metadata": {},
+   "source": [
+    "## Spearman Correlation Matrix – Key Observations\n",
+    "\n",
+    "###  Why Spearman?\n",
+    "\n",
+    "* Captures **monotonic non-linear relationships**\n",
+    "* Robust to skewed & non-normal distributions\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  Correlation Table Summary\n",
+    "\n",
+    "| Feature Pair                        | Spearman ρ | Interpretation                       |\n",
+    "| ----------------------------------- | ---------- | ------------------------------------ |\n",
+    "| technical_score ↔ slope_strength    | **0.414**  | Strong positive monotonic relation   |\n",
+    "| technical_score ↔ pattern_symmetry  | **0.362**  | Trend quality linked to structure    |\n",
+    "| edge_density ↔ candlestick_variance | **0.426**  | Volatile price action → noisy charts |\n",
+    "| lookback_days ↔ others              | ~0         | Largely independent                  |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  Low Correlation with Lookback Days\n",
+    "\n",
+    "* lookback_days shows **near-zero correlation** with most features.\n",
+    "* Indicates:\n",
+    "\n",
+    "  * Acts as a **contextual parameter**, not a signal.\n",
+    "  * Useful for **conditioning**, not prediction.\n",
+    "\n",
+    "**Interpretation**\n",
+    "Keep lookback_days, but expect **weak feature importance**.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Feature Dependency Insights\n",
+    "\n",
+    "###  Technical Score as a Central Feature\n",
+    "\n",
+    "* Positively correlated with:\n",
+    "\n",
+    "  * slope_strength (trend magnitude)\n",
+    "  * pattern_symmetry (structural consistency)\n",
+    "* Negatively correlated with:\n",
+    "\n",
+    "  * candlestick_variance (noise)\n",
+    "\n",
+    "**Interpretation**\n",
+    "`technical_score` likely acts as a **summary signal**, aggregating multiple technical behaviors.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  Volatility & Structure Relationship\n",
+    "\n",
+    "* edge_density ↔ candlestick_variance correlation indicates:\n",
+    "\n",
+    "  * Chaotic markets produce visually complex charts.\n",
+    "* These features may capture **regime-specific behavior**, not direction directly.\n",
+    "\n",
+    "**Interpretation**\n",
+    "Good candidates for **interaction with market_regime**.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Multicollinearity Assessment\n",
+    "\n",
+    "* No correlations exceed **0.7**\n",
+    "* No strong redundancy detected\n",
+    "* Safe for:\n",
+    "\n",
+    "  * Tree-based models\n",
+    "  * Regularized linear models\n",
+    "\n",
+    "**Decision**\n",
+    "\n",
+    "No feature removal required based on correlation alone.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3da85547",
+   "metadata": {},
+   "source": [
+    "### PreProcessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c7a7826b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# one-hot encoding for categorical variables and scaling numerical features\n",
+    "\n",
+    "numerical_features = ['lookback_days','technical_score','edge_density',\n",
+    "                      'slope_strength','candlestick_variance','pattern_symmetry']\n",
+    "categorical_features = ['asset_type', 'market_regime']\n",
+    "binary_features = ['high_volatility','trend_continuation']\n",
+    "\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', StandardScaler(), numerical_features),\n",
+    "        ('cat', OneHotEncoder(drop='first'), categorical_features) # set drop='first' to avoid dummy variable trap\n",
+    "        # binary features are already numeric 0/1\n",
+    "    ],\n",
+    "    remainder='passthrough'  # keep binary features as-is\n",
+    ")\n",
+    "\n",
+    "X_prepared = preprocessor.fit_transform(df.drop(columns='future_trend'))\n",
+    "y = df['future_trend']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a1b684",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.9141643 , -0.12386556,  0.34463563,  0.07679319,  0.50994241,\n",
+       "        0.89042403,  1.        ,  0.        ,  1.        ,  0.        ,\n",
+       "        0.        ,  1.        ])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_prepared[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4861630",
+   "metadata": {},
+   "source": [
+    "# 4.2 Model Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "513f9fe4",
+   "metadata": {},
+   "source": [
+    "### Split using Startified Sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "781212cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train class distribution: [ 10 150]\n",
+      "Test class distribution: [ 3 37]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train-test split\n",
+    "\n",
+    "# X_prepared and y from preprocessing (numerical scaled, categorical one-hot encoded, binary included)\n",
+    "# Example: X_prepared, y as prepared before\n",
+    "\n",
+    "# Split into 80% train, 20% test\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_prepared,         # predictors\n",
+    "    y,                  # target\n",
+    "    test_size=0.2,      # 20% test set\n",
+    "    random_state=42,    # reproducible\n",
+    "    stratify=y          # maintain class proportions\n",
+    ")\n",
+    "\n",
+    "# Check class distribution\n",
+    "\n",
+    "print(\"Train class distribution:\", np.bincount(y_train))\n",
+    "print(\"Test class distribution:\", np.bincount(y_test))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "94986aeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.metrics import (\n",
+    "    accuracy_score, precision_score, recall_score, f1_score,\n",
+    "    roc_auc_score, precision_recall_curve, average_precision_score\n",
+    ")\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Dictionary to store metrics\n",
+    "model_metrics = {}\n",
+    "\n",
+    "# Reusable function to train, evaluate and plot\n",
+    "def evaluate_model(model, model_name, X_train, y_train, X_test, y_test):\n",
+    "    model.fit(X_train, y_train)\n",
+    "    \n",
+    "    y_pred = model.predict(X_test)\n",
+    "    y_proba = model.predict_proba(X_test)[:,1] if hasattr(model, \"predict_proba\") else model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate metrics\n",
+    "    metrics = {\n",
+    "        \"accuracy\": accuracy_score(y_test, y_pred),\n",
+    "        \"precision\": precision_score(y_test, y_pred, zero_division=0),\n",
+    "        \"recall\": recall_score(y_test, y_pred, zero_division=0),\n",
+    "        \"f1_score\": f1_score(y_test, y_pred, zero_division=0),\n",
+    "        \"roc_auc\": roc_auc_score(y_test, y_proba) if hasattr(model, \"predict_proba\") else None\n",
+    "    }\n",
+    "    \n",
+    "    # Precision-Recall curve\n",
+    "    if hasattr(model, \"predict_proba\"):\n",
+    "        precision, recall, thresholds = precision_recall_curve(y_test, y_proba)\n",
+    "        metrics[\"pr_curve\"] = (precision, recall, thresholds)\n",
+    "        \n",
+    "        # Plot Precision vs Recall vs Threshold\n",
+    "        plt.figure(figsize=(7,5))\n",
+    "        plt.plot(thresholds, precision[:-1], label='Precision', color='blue')\n",
+    "        plt.plot(thresholds, recall[:-1], label='Recall', color='green')\n",
+    "        plt.xlabel('Threshold')\n",
+    "        plt.ylabel('Score')\n",
+    "        plt.title(f'Precision and Recall vs Threshold - {model_name}')\n",
+    "        plt.legend()\n",
+    "        plt.grid(True)\n",
+    "        plt.show()\n",
+    "    \n",
+    "    # Store metrics\n",
+    "    model_metrics[model_name] = metrics\n",
+    "    \n",
+    "    # Print metrics\n",
+    "    print(f\"\\nMetrics for {model_name}:\")\n",
+    "    for k,v in metrics.items():\n",
+    "        if k != \"pr_curve\":\n",
+    "            print(f\"{k}: {v:.4f}\")\n",
+    "    # Confusion Matrix\n",
+    "    cm = confusion_matrix(y_test, y_pred)\n",
+    "    plt.figure(figsize=(5,4))\n",
+    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
+    "    plt.xlabel('Predicted')\n",
+    "    plt.ylabel('Actual')\n",
+    "    plt.title(f'Confusion Matrix - {model_name}')\n",
+    "    plt.show()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a75c6872",
+   "metadata": {},
+   "source": [
+    "### Simple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7654d7a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Metrics for Logistic Regression (no regularization):\n",
+      "accuracy: 0.9500\n",
+      "precision: 0.9730\n",
+      "recall: 0.9730\n",
+      "f1_score: 0.9730\n",
+      "roc_auc: 0.9550\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LlxLtPy4uDsOGDcPQoUPx6aef6iz7+uuv0b59e9jb28PCwgKWlpZYt24dLl++rNe5lOdjLO/x9Mcff5SoJskhXq1aNdja2mq77IqTkZEBAHB3d8+3zMPDQ7s8T9WqVfOtp1QqtV1wZcHb2xvff/89XFxcMGHCBHh7e8Pb2xtLliwpcruMjIxCzyNv+fNePJe88QMlOReFQoERI0Zg8+bNWLlyJerVq4eOHTsWuO5PP/2EwMBAAMCaNWvw448/IjExETNnzizxcQs6z6JqDAsLQ1ZWFtzc3CTdC09JSYGlpaXOlBck+srIyICbm1u+cREuLi6wsLDQ/n4yMjJgYWEBZ2dnnfVcXV2LPYaTkxPi4uLQokULzJgxA40bN4aHhwfCw8Pz3duTIu8ecY0aNUq8bZ733nsPiYmJOHXqFBYtWoTs7Gz06dNH5/F4+/Zt7Nu3L981b9y4MQBowzEjI6PA61DYtbG1tYWjo6POvNu3b+P+/fuwsrLKdzy1Wq09lpTn4bBhwxAdHY0bN26gf//+cHFxgY+PD44ePVro9SjuNSc3Nxd//fWXznx9n6tPnjwp8s1Xcfst6etjYV7c/vbt2xBCwNXVNd/vICEhQef3DRT8+5XyfJAqNTUVHTt2xB9//IElS5bghx9+QGJiIpYvXw4g/3UuyesPACQlJSEkJAQdO3bEunXrdJbt2rULb7zxBqpXr47NmzcjPj5e2xjKysrS63zK8zGW93gqaeZZSF3R3NwcXbt2xcGDB3Hr1q1iX3zyCk9LS8u37p9//olq1aqVqNCi5J28RqPRGXBX0Lv3jh07omPHjsjJycHZs2exdOlSTJo0Ca6urnjzzTcL3H/VqlWRlpaWb37eQJWyPJfnhYWFYdasWVi5cmW+d5jPi4mJgaWlJfbv36/zwlLQ5x6LU9AAwcKkpaVhwoQJaNGiBZKSkjB16lR8+eWXRW7j4eGBxMREnXn169cvcZ3Pq1q1Ks6cOQMhhE796enpePbsmfb3U7VqVTx79gz37t3TCXK1Wi3pOE2bNkVMTAyEEPj111+xYcMGfPLJJ7CxscF//vOfEtX88ssvA4CkQUyFqVGjhnYwW/v27eHm5oahQ4ciPDwcy5YtA/DPY7NZs2aFPn7y3ohWrVoVP/30U77lhV2bgh4neYO4Dh06VOA2Dg4O2v9LeR6OGDECI0aMQGZmJk6ePInw8HD06tULV65cgaenZ779P/+a86I///xT+3n6slCtWrVSfSdGWb0+vvh7qFatGhQKBX744YcCBx/nzcs7fkHjL9RqtU5r/PnX1+dJ6R3Zs2cPMjMzsWvXLp3f2fnz5yWdT1Fu3bqF7t27o1atWti5cycsLS11lm/evBleXl7Yvn27zn5fPI+SKM/HWN7jqaR5UqLu9OnTp0MIgdGjRxc4ECw7Oxv79u0DAG2XSN7AtDyJiYm4fPmydjRrWch7wP3666868/NqKYi5uTl8fHy07wj/+9//Frpu165dcfz48XyjSzdt2gRbW1u0a9dOz8qLVr16dXzwwQcIDg5GaGhooespFApYWFjA3NxcO+/Jkyf46quv8q1bVr0bOTk5GDRoEBQKBQ4ePAiVSoWlS5di165dRW5nZWWF1q1b60zPv7jro2vXrnj8+HG+Ny15g1fyHmv+/v4AgO3bt+usFxMTU6LjKRQKNG/eHIsXL8ZLL72k89iRen3r1aunvV1SmheV5w0ZMgQBAQFYs2aNttu2V69euHjxIry9vfNd99atW2tD3N/fH48ePcLBgwd19lmSa9OrVy9kZGQgJyenwGMV9GZNyvPQzs4OQUFBmDlzJp4+fYqkpKQCj1+/fn1Ur14dW7du1blFk5mZiZ07d2pHE5eFBg0aSL4dV5Dyen3s1asXhBD4448/Cvwd5N3u8vHxgVKpzPdcSEhIyHfbr7DX17179xZbT154Pv+GQgiBNWvWlPjcnvfgwQMEBQVBoVDgwIED+XqF8o5tZWWlE+BqtTrf6PS8+qQ8b8vzMZb3eMobmCeV5JY4APj6+iIqKgrjx49Hq1atMG7cODRu3BjZ2dk4d+4cVq9ejSZNmiA4OBj169fH22+/jaVLl8LMzAxBQUFISUnBxx9/jJo1a0oaASpVjx494OzsjJEjR+KTTz6BhYUFNmzYgJs3b+qst3LlShw/fhw9e/ZErVq1kJWVpR0B/tprrxW6//DwcO29xVmzZsHZ2RlbtmzBd999h8jISDg5OZXZubxo/vz5xa7Ts2dPfP755xg8eDDefvttZGRkYNGiRQW+E89rTW7fvh21a9eGtbW1pPvYLwoPD8cPP/yAI0eOwM3NDVOmTEFcXBxGjhyJli1bwsvLq8T7LMq1a9fwzTff5JvfqFEjDB8+HMuXL0doaChSUlLQtGlTnDp1ChEREejRo4f2d9u9e3e0b98eU6ZMwcOHD9GqVSvEx8drw/75e1wv2r9/P1asWIGQkBDUrl0bQgjs2rUL9+/fR7du3bTrNW3aFLGxsdi3bx/c3d3h4OBQaE/D8uXLERwcjHbt2uH9999HrVq1kJqaisOHD2PLli16XacFCxbAx8cHc+fOxdq1a/HJJ5/g6NGj8PPzw7vvvov69esjKysLKSkpOHDgAFauXIkaNWogNDQUixcvxtChQzFv3jzUqVMHBw8exOHDh4u9NnnefPNNbNmyBT169MB7772Htm3bwtLSErdu3cKJEyfQp08f9O3bV9LzcPTo0bCxsUH79u3h7u4OtVoNlUoFJycntGnTpsDjm5mZITIyEkOGDEGvXr0wZswYaDQaLFy4EPfv35f0XJIqICAA0dHRuHLlis6nAaQqr9fH9u3b4+2338aIESNw9uxZdOrUCXZ2dkhLS8OpU6fQtGlTjBs3Ds7Ozpg8eTJUKhWqVKmCvn374tatW5gzZw7c3d11ft9t2rRB/fr1MXXqVDx79gxVqlTB7t27CxyJ/aJu3brBysoKgwYNwocffoisrCxERUXl63IuqcGDB+PSpUtYvXo1bt68qfNaX6NGDdSoUQO9evXCrl27MH78eAwYMAA3b97E3Llz4e7ujqtXr+rsT+rztjwfYwkJCUV+4qFQJRoG9/+dP39ehIaGilq1agkrKythZ2cnWrZsKWbNmiXS09O16+Xk5IgFCxaIevXqCUtLS1GtWjUxdOjQfN+G5u/vLxo3bpzvOAWNdEQhI5V/+ukn4efnJ+zs7ET16tVFeHi4WLt2rc6oyvj4eNG3b1/h6ekplEqlqFq1qvD39xd79+7Nd4znRyoLIcSFCxdEcHCwcHJyElZWVqJ58+Y6I3aF+Hck49dff60zP2808Yvrv+j50elFKWiEeXR0tKhfv75QKpWidu3aQqVSiXXr1uUbVZqSkiICAwOFg4ODAKC9voXV/vyyvBGaR44cEWZmZvmuUUZGhqhVq5Zo06aN0Gg0RZ5DSQAodMqrISMjQ4wdO1a4u7sLCwsL4enpKaZPn57vS4nu3bsnRowYIV566SVha2srunXrJhISEgQAsWTJEu16L47I/d///icGDRokvL29hY2NjXBychJt27YVGzZs0Nn/+fPnRfv27YWtra3OKN/CRvjHx8eLoKAg4eTkJJRKpfD29hbvv/9+kdejsC97yfP6668LCwsL8fvvvwshhLhz54549913hZeXl7C0tBTOzs6iVatWYubMmeLx48fa7VJTU0W/fv2Evb29cHBwEP379xcHDhwQAHS+xCk0NFTY2dkVeOzs7GyxaNEi0bx5c2FtbS3s7e1FgwYNxJgxY8TVq1e151zc83Djxo2ic+fOwtXVVVhZWQkPDw/xxhtv6Hz6obBrumfPHuHj4yOsra2FnZ2d6Nq1q/jxxx911skbOXznzh2d+YWNxH7RgwcPhL29fb6Rx4U9hwuqVerrY0EKqz9PdHS08PHxEXZ2dsLGxkZ4e3uL4cOHi7Nnz2rXyc3NFfPmzRM1atQQVlZWolmzZmL//v2iefPmom/fvjr7u3LliggMDBSOjo7i5ZdfFhMnTtR+2qO40en79u3TPh6qV68uPvjgA3Hw4MF82xaWA3nLnn/N8/T0LPY1QQgh5s+fL1555RWhVCpFw4YNxZo1awr8NFNJn7fl8Rjr2LGjCA4OLvD8i6IQopRDg4lM3NatWzFkyBD8+OOP8PPzM3Y5shIREYGPPvoIqamppRqEVxFNnDgRx44dQ1JSUonu5cpZcnIyGjRogPDwcJ0v1qHyde3aNdStWxeHDx/W6dmTgiFOlcq2bdvwxx9/oGnTpjAzM0NCQgIWLlyIli1blnqUvKnLGwzXoEEDZGdn4/jx4/jyyy8xcOBAnS/HoH/cvn0b9erVw7p16/J9vNEU/PLLL9i2bRv8/Pzg6OiI3377DZGRkXj48CEuXrxYpqPUqWgjRozArVu3ivz0RWFKdE+cyNQ5ODggJiYG8+bNQ2ZmJtzd3REWFoZ58+YZuzSjs7W1xeLFi5GSkgKNRoNatWph2rRp+Oijj4xdmiy5urpiy5Ytpb6/ayx2dnY4e/Ys1q1bh/v378PJyQkBAQH49NNPGeAG9OzZM3h7e2P69Ol6bc+WOBERkYkqt29sIyIiovLFECciIjJRDHEiIiITxRAnIiIyURydbgSPsnKNXQJVEpYWfJ9OhmNdholi0/Idvbd9cm5Z2RUicwxxIiKSHwXfgErBECciIvmpIN+CV94Y4kREJD9siUvCq0RERGSiGOJERCQ/CoX+UwlERUWhWbNmcHR0hKOjI3x9fXHw4EHt8rCwMCgUCp2pXbt2ZX22emN3OhERyY+ButNr1KiB+fPno06dOgCAjRs3ok+fPjh37hwaN24MAOjevTvWr1+v3cbKysogtUnBECciIvkx0MC24OBgnZ8//fRTREVFISEhQRviSqUSbm5uBqmnpNidTkRE8qMw03vSaDR4+PChzqTRaIo9ZE5ODmJiYpCZmQlfX1/t/NjYWLi4uKBevXoYPXo00tPTy/PMS4QhTkRE8lOKe+IqlQpOTk46k0qlKvRQFy5cgL29PZRKJcaOHYvdu3ejUaNGAICgoCBs2bIFx48fx2effYbExER06dJF0psCQ+CfIjUCfmMbGQq/sY0MqUy/sc33P3pvez92Tr6QVSqVUCqVBa7/9OlTpKam4v79+9i5cyfWrl2LuLg4bZA/Ly0tDZ6enoiJiUG/fv30rrGs8J44ERHJTykGthUV2AWxsrLSDmxr3bo1EhMTsWTJEqxatSrfuu7u7vD09MTVq1f1rq8sMcSJiEh+jPiNbUKIQrvLMzIycPPmTbi7uxu4qoIxxImISH4M9BGzGTNmICgoCDVr1sSjR48QExOD2NhYHDp0CI8fP8bs2bPRv39/uLu7IyUlBTNmzEC1atXQt29fg9RXHIY4ERHJj4Fa4rdv38awYcOQlpYGJycnNGvWDIcOHUK3bt3w5MkTXLhwAZs2bcL9+/fh7u6Ozp07Y/v27XBwcDBIfcXhwDYj4MA2MhQObCNDKtOBbZ1m673tk5P6b2tq+AwnIiIyUexOJyIi+eFfMZOEIU5ERPJjxr8nLgVDnIiI5IctcUkY4kREJD9G/Jy4KWGIExGR/LAlLgmvEhERkYliS5yIiOSH3emSMMSJiEh+2J0uCUOciIjkhy1xSRjiREQkP2yJS8IQJyIi+WFLXBK+1SEiIjJRbIkTEZH8sDtdEoY4ERHJD7vTJWGIExGR/LAlLglDnIiI5IchLglDnIiI5Ifd6ZLwrQ4REZGJYkuciIjkh93pkjDEiYhIftidLglDnIiI5IctcUkY4kREJD9siUvCECciItlRMMQlYX8FERGRiWJLnIiIZIctcWkY4kREJD/McEkY4kREJDtsiUvDECciItlhiEvDECciItlhiEvD0elEREQmii1xIiKSHbbEpWGIExGR/DDDJWF3OhERyY5CodB7KomoqCg0a9YMjo6OcHR0hK+vLw4ePKhdLoTA7Nmz4eHhARsbGwQEBCApKamsT1dvDHEiIpIdQ4V4jRo1MH/+fJw9exZnz55Fly5d0KdPH21QR0ZG4vPPP8eyZcuQmJgINzc3dOvWDY8ePSqP0y4xhRBCGLuIyuZRVq6xS6BKwtKC79PJcKzL8Aat87Ctem9776vBpTu2szMWLlyIt956Cx4eHpg0aRKmTZsGANBoNHB1dcWCBQswZsyYUh2nLPAZTkREFYpGo8HDhw91Jo1GU+x2OTk5iImJQWZmJnx9fZGcnAy1Wo3AwEDtOkqlEv7+/jh9+nR5noJkDHEiIpKd0nSnq1QqODk56UwqlarQY124cAH29vZQKpUYO3Ysdu/ejUaNGkGtVgMAXF1dddZ3dXXVLjM2jk4nIiL5KcXo9OnTp2Py5Mk685RKZaHr169fH+fPn8f9+/exc+dOhIaGIi4u7t9SXrjPLoSQzUfgGOJERCQ7pQlJpVJZZGi/yMrKCnXq1AEAtG7dGomJiViyZIn2PrharYa7u7t2/fT09Hytc2NhdzoREcmOoUanF0QIAY1GAy8vL7i5ueHo0aPaZU+fPkVcXBz8/PxKfZyywJY4ERHJjqG6q2fMmIGgoCDUrFkTjx49QkxMDGJjY3Ho0CEoFApMmjQJERERqFu3LurWrYuIiAjY2tpi8ODSjYAvKwxxIiKqtG7fvo1hw4YhLS0NTk5OaNasGQ4dOoRu3boBAD788EM8efIE48ePx19//QUfHx8cOXIEDg4ORq78H/ycuBHwc+JkKPycOBlSWX5O3GXkDr23TV/3RtkVInNsiRMRkezIZfS33DHEiYhIdhji0jDEiYhIdhji0jDEiYhIdhji0nDUCxERkYliS5yIiOSHDXFJGOJERCQ77E6XhiFORESywxCXhiFORESywxCXhgPbiIiITBRb4kREJD9siEvCECdZW79uNU4cO4qU5OtQKq3RrEVLTJw0Ba+84mXs0qiC+flsIjZEr8PlSxdx584dLP5yObp0fc3YZVVa7E6Xht3pJGv/PZuI1wcOxvqvYrB81TrkPHuGd8aOxJO//zZ2aVTBPHnyN+rXr4//zJxl7FIIxv174qaELXGStaVRa3R+Dv8kAt06t8fly0l4tVUbI1VFFVGHjv7o0NHf2GXQ/1fZwlhfDHEyKY8fPwIAODo6GbkSIipPDHFpGOLFuHXrFqKionD69Gmo1WooFAq4urrCz88PY8eORc2aNY1dYqUhhMDnixagRctWqFO3nrHLISIyOoZ4EU6dOoWgoCDUrFkTgYGBCAwMhBAC6enp2LNnD5YuXYqDBw+iffv2he5Do9FAo9HozHsqLKFUKsu7/AonUjUXv1/9DWs3bDF2KURU3tgQl4QhXoT3338fo0aNwuLFiwtdPmnSJCQmJha6D5VKhTlz5ujM+8/MWZjxUXiZ1lrRRarm4WTsCayO/gqurm7GLoeIyhm706VhiBfh4sWL2Lx5c6HLx4wZg5UrVxa5j+nTp2Py5Mk6854KyzKprzIQQiBSNQ+xx7/HqnUbUb1GDWOXREQGwBCXhiFeBHd3d5w+fRr169cvcHl8fDzc3d2L3IdSqczXdf4oK7fMaqzoFkR8gkMHv8NnXyyDrZ0d7t69AwCwt3eAtbW1kaujiuTvzEykpqZqf/7j1i387/JlODk5wd3Dw4iVVU7McGkY4kWYOnUqxo4di59//hndunWDq6srFAoF1Go1jh49irVr1+KLL74wdpkV2jc7YgAAY0aG6swP/yQCwX36GqMkqqCSki5i1Ijh2p8XRaoAAL379MXciPnGKqvSYktcGoUQQhi7CDnbvn07Fi9ejJ9//hk5OTkAAHNzc7Rq1QqTJ0/GG2+8UeJ9siVOhmJpwe9zIsOxLsNmYd0PDum97dWF3cuuEJljiEuUnZ2Nu3fvAgCqVasGS0v972szxMlQGOJkSGUZ4vU+1D/Er0RWnhBnd7pElpaWxd7/JiKissHudGkY4kREJDvMcGkY4kREJDtmZkxxKRjiREQkO2yJS8NRL0RERCaKLXEiIpIdDmyThiFORESywwyXhiFORESyw5a4NAxxIiKSHYa4NAxxIiKSHWa4NBydTkRElZZKpUKbNm3g4OAAFxcXhISE4LffftNZJywsDAqFQmdq166dkSrWxRAnIiLZeTE0SzKVRFxcHCZMmICEhAQcPXoUz549Q2BgIDIzM3XW6969O9LS0rTTgQMHyvJ09cbudCIikh1DdacfOqT7h1bWr18PFxcX/Pzzz+jUqZN2vlKphJubm2GKKgG2xImISHZK0xLXaDR4+PChzqTRaCQd98GDBwAAZ2dnnfmxsbFwcXFBvXr1MHr0aKSnp5f5OeuDIU5ERLKjUOg/qVQqODk56UwqlarYYwohMHnyZHTo0AFNmjTRzg8KCsKWLVtw/PhxfPbZZ0hMTESXLl0kvzEoT/x74kbAvydOhsK/J06GVJZ/T7zNp7F6b3tqqm++gFUqlVAqlUVuN2HCBHz33Xc4deoUatSoUeh6aWlp8PT0RExMDPr166d3nWWB98SJiKhCkRLYL5o4cSL27t2LkydPFhngAODu7g5PT09cvXq1NGWWCYY4ERHJjqEGtgkhMHHiROzevRuxsbHw8vIqdpuMjAzcvHkT7u7uBqiwaOxrIyIi2THUR8wmTJiAzZs3Y+vWrXBwcIBarYZarcaTJ08AAI8fP8bUqVMRHx+PlJQUxMbGIjg4GNWqVUPfvn3L49RLhC1xIiKSHUO1xKOiogAAAQEBOvPXr1+PsLAwmJub48KFC9i0aRPu378Pd3d3dO7cGdu3b4eDg4NhiiwCQ5yIiGTHUN+dXtzYbhsbGxw+fNggteiDIU5ERLLD706XhvfEiYiITBRb4kREJDv8U6TSMMSJiEh2mOHSMMSJiEh22BKXhiFORESywxCXhiFORESywwyXhqPTiYiITBRb4kREJDvsTpeGIU5ERLLDDJeGIU5ERLLDlrg0DHEiIpIdZrg0DHEiIpIdM6a4JBydTkREZKLYEiciItlhQ1wahjgREckOB7ZJwxAnIiLZMWOGS8IQJyIi2WFLXBqGOBERyQ4zXBqOTiciIjJRbIkTEZHsKMCmuBQMcSIikh0ObJOGIU5ERLLDgW3SMMSJiEh2mOHSMMSJiEh2+N3p0nB0OhERkYliS5yIiGSHDXFpGOJERCQ7HNgmDUOciIhkhxkuDUOciIhkhwPbpGGIExGR7DDCpakQIb53717J6/bu3bscKyEiIjKcChHiISEhktZTKBTIyckp32KIiKjUOLBNmgrxOfHc3FxJEwOciMg0mCn0n0pCpVKhTZs2cHBwgIuLC0JCQvDbb7/prCOEwOzZs+Hh4QEbGxsEBAQgKSmpDM9WfxUixImIqGJRKBR6TyURFxeHCRMmICEhAUePHsWzZ88QGBiIzMxM7TqRkZH4/PPPsWzZMiQmJsLNzQ3dunXDo0ePyvq0S0whhBDGLqKsZWZmIi4uDqmpqXj69KnOsnfffddIVf3rUVausUugSsLSgu/TyXCsy/AG7bAtv+i97VdDmuu97Z07d+Di4oK4uDh06tQJQgh4eHhg0qRJmDZtGgBAo9HA1dUVCxYswJgxY/Q+VlmoEPfEn3fu3Dn06NEDf//9NzIzM+Hs7Iy7d+/C1tYWLi4usghxIiIqWmnuiWs0Gmg0Gp15SqUSSqWy2G0fPHgAAHB2dgYAJCcnQ61WIzAwUGdf/v7+OH36tNFDvMK9TX///fcRHByMe/fuwcbGBgkJCbhx4wZatWqFRYsWGbs8IiIqZyqVCk5OTjqTSqUqdjshBCZPnowOHTqgSZMmAAC1Wg0AcHV11VnX1dVVu8yYKlxL/Pz581i1ahXMzc1hbm4OjUaD2rVrIzIyEqGhoejXr5+xSyQiomKUdIDa86ZPn47JkyfrzJPSCn/nnXfw66+/4tSpU/mWvdgzIISQxQj6ChfilpaW2gvr6uqK1NRUNGzYEE5OTkhNTTVydUREJEVpAlJq1/nzJk6ciL179+LkyZOoUaOGdr6bmxuAf1rk7u7u2vnp6en5WufGUOG601u2bImzZ88CADp37oxZs2Zhy5YtmDRpEpo2bWrk6oiISApFKaaSEELgnXfewa5du3D8+HF4eXnpLPfy8oKbmxuOHj2qnff06VPExcXBz89Pr3MrSxUuxCMiIrTvlubOnYuqVati3LhxSE9Px+rVq41cHRERSWGmUOg9lcSECROwefNmbN26FQ4ODlCr1VCr1Xjy5AmAf3oEJk2ahIiICOzevRsXL15EWFgYbG1tMXjw4PI49RKpkB8xkzt+xIwMhR8xI0Mqy4+Yjdp+Ue9t1w5sInndwrrt169fj7CwMAD/tNbnzJmDVatW4a+//oKPjw+WL1+uHfxmTAxxI2CIk6EwxMmQyjLER+/QP8TXvGH8cDWUCjewzcvLq8gBEdevXzdgNUREpA85jPw2BRUuxCdNmqTzc3Z2Ns6dO4dDhw7hgw8+ME5RRERUIsxwaSpciL/33nsFzl++fLl21DoREclbSQeoVVaV5oZZUFAQdu7caewyiIhIAoVC/6kyqTQh/s0332i/C5eIiKgiqHDd6S1bttQZECGEgFqtxp07d7BixQojVkZERFJxYJs0FS7E+/Tpo/PLNzMzw8svv4yAgAA0aNDAiJX9ix/7IUOp0uYdY5dAlciTc8vKbF98lZSmwoX47NmzjV0CERGVElvi0lS4Nzvm5uZIT0/PNz8jIwPm5uZGqIiIiErKTKH/VJlUuJZ4YV9Ap9FoYGVlZeBqiIhIH5UtjPVVYUL8yy+/BPBPF8zatWthb2+vXZaTk4OTJ0/K5p44ERFRWagwIb548WIA/7TEV65cqdN1bmVlhVdeeQUrV640VnlERFQCvCcuTYUJ8eTkZAD//A3xXbt2oUqVKkauiIiI9MXudGkqTIjnOXHihLFLICKiUmJDXJoKNzp9wIABmD9/fr75CxcuxOuvv26EioiIqKTMFAq9p8qkwoV4XFwcevbsmW9+9+7dcfLkSSNUREREJWVWiqkyqXDn+/jx4wI/SmZpaYmHDx8aoSIiIqLyUeFCvEmTJti+fXu++TExMWjUqJERKiIiopLiXzGTpsINbPv444/Rv39/XLt2DV26dAEAHDt2DFu3bsU333xj5OqIiEiKynZvW18VLsR79+6NPXv2ICIiAt988w1sbGzQvHlzHD9+HI6OjsYuj4iIJGCGS1PhQhwAevbsqR3cdv/+fWzZsgWTJk3CL7/8gpycHCNXR0RExeHnxKWpcPfE8xw/fhxDhw6Fh4cHli1bhh49euDs2bPGLouIiCTgR8ykqVAt8Vu3bmHDhg2Ijo5GZmYm3njjDWRnZ2Pnzp0c1EZERBVOhWmJ9+jRA40aNcKlS5ewdOlS/Pnnn1i6dKmxyyIiIj1wdLo0FaYlfuTIEbz77rsYN24c6tata+xyiIioFHhPXJoK0xL/4Ycf8OjRI7Ru3Ro+Pj5YtmwZ7ty5Y+yyiIhID4pS/KtMKkyI+/r6Ys2aNUhLS8OYMWMQExOD6tWrIzc3F0ePHsWjR4+MXSIREUlkptB/qkwqTIjnsbW1xVtvvYVTp07hwoULmDJlCubPnw8XFxf07t3b2OUREZEEDHFpKlyIP69+/fqIjIzErVu3sG3bNmOXQ0REVKYqzMC2opibmyMkJAQhISHGLoWIiCRQVLZh5nqqFCFORESmpbJ1i+uLIU5ERLLDhrg0DHEiIpKdyvb1qfqq0APbiIjINBlydPrJkycRHBwMDw8PKBQK7NmzR2d5WFgYFAqFztSuXbuyOdFSYogTEVGllpmZiebNm2PZsmWFrtO9e3ekpaVppwMHDhiwwsKxO52IiGTHkL3pQUFBCAoKKnIdpVIJNzc3A1UkHVviREQkO2ZQ6D1pNBo8fPhQZ9JoNKWqJzY2Fi4uLqhXrx5Gjx6N9PT0MjrT0mGIExGR7JTmr5ipVCo4OTnpTCqVSu9agoKCsGXLFhw/fhyfffYZEhMT0aVLl1K/MSgL7E4nIiLZKc3nxKdPn47JkyfrzFMqlXrvb+DAgdr/N2nSBK1bt4anpye+++479OvXT+/9lgWGOBERyU5pPmKmVCpLFdrFcXd3h6enJ65evVpux5CK3elEREQlkJGRgZs3b8Ld3d3YpbAlTkRE8mPI0emPHz/G77//rv05OTkZ58+fh7OzM5ydnTF79mz0798f7u7uSElJwYwZM1CtWjX07dvXcEUWgiFORESyY8hvbDt79iw6d+6s/TnvfnpoaCiioqJw4cIFbNq0Cffv34e7uzs6d+6M7du3w8HBwWA1FoYhTkREsmPIlnhAQACEEIUuP3z4sOGKKSGGOBERyQ4HbEnDECciItnh3xOXhm92iIiITBRb4kREJDtsh0vDECciItnh3xOXhiFORESywwiXhiFORESyw4a4NAxxIiKSHY5Ol4aj04mIiEwUW+JERCQ7bGFKwxAnIiLZYXe6NAxxIiKSHUa4NAxxIiKSHbbEpWGIExGR7PCeuDS8TkRERCaKLXEiIpIddqdLwxAnIiLZYYRLwxAnIiLZYUNcGoY4ERHJjhnb4pIwxImISHbYEpeGo9OJiIhMFFviREQkOwp2p0vCECciItlhd7o0DHEiIpIdDmyThiFORESyw5a4NAxxIiKSHYa4NBydTkREZKLYEiciItnh6HRpGOJERCQ7ZsxwSRjiREQkO2yJS8MQJyIi2eHANmk4sI2IiMhEsSVORESyw+50adgSJ1n7+WwiJo4fi9cCOqB54/o4fux7Y5dEFcDo1zvgp+3TcfuHhbj9w0LEbpyCwPaNdNap7+WKr78YA/XJhUg/tQhxG6egplsVI1Vc+Zgp9J9K6uTJkwgODoaHhwcUCgX27Nmjs1wIgdmzZ8PDwwM2NjYICAhAUlJS2ZxoKTHESdaePPkb9evXx39mzjJ2KVSB/HH7Pj5e+i3aD1mI9kMWIvanK/h68dtoWNsNAOBVoxqORU/GlWQ1/m/0ErQdqIJqzSFkabKNXHnloSjFv5LKzMxE8+bNsWzZsgKXR0ZG4vPPP8eyZcuQmJgINzc3dOvWDY8ePSrtaZYau9NJ1jp09EeHjv7GLoMqmAMnL+r8PHv5Pox+vQPaNvPC5etqzHknGIdPJWHmkm+166T8kWHoMis1Qw5sCwoKQlBQUIHLhBD44osvMHPmTPTr1w8AsHHjRri6umLr1q0YM2aM4QotAFviRFSpmZkp8Pr/tYKdjRXO/JoMhUKB7h0a42pqOvYun4Abx1Q4uWkqggOaGbvUSkVRikmj0eDhw4c6k0aj0auO5ORkqNVqBAYGaucplUr4+/vj9OnTep9fWWGIE1Gl1LiOB+78+BkenPkCX84ciIFT1uB/19VwcbaHg501po7ohqOnLyF43DLsPfELYj4bhQ6t6hi7bJJApVLByclJZ1KpVHrtS61WAwBcXV115ru6umqXGRO700vh5s2bCA8PR3R0dKHraDSafO8AhbkSSqWyvMsjoiJcSbkNnzdVeMnBFiFdW2DNJ8MQOGoJHjx6AgDYH3sBS7ecAAD8euUP+DSvjdEDOuDUz78bs+xKw6wU/enTp0/H5MmTdeaV9jVX8UI9Qoh884yBLfFSuHfvHjZu3FjkOgW9I1y4QL93hERUdrKf5eD6zbv476VUzFq6Fxeu/IEJgwJw96/HyM7OweXraTrr/3ZdzdHpBlSa7nSlUglHR0edSd8Qd3P7Z7Dji63u9PT0fK1zY2BLvAh79+4tcvn169eL3UdB7wiFOVvhRHKjgAJKKwtkP8vBz5duoJ6n7gt0XU8XpKb9ZaTqKiHjN3IBAF5eXnBzc8PRo0fRsmVLAMDTp08RFxeHBQsWGLk6hniRQkJCoFAoIIQodJ3iulOUyvxd51nPyqS8SuHvzEykpqZqf/7j1i387/JlODk5wd3Dw4iVkSmb804wjvx4CTfVf8HBzhqv/18rdGpdF70nrAAALN74Pb5a8BZO/fd3xJ29gkC/RujRqQn+b/QSI1deeRjyy14eP36M33//9zZJcnIyzp8/D2dnZ9SqVQuTJk1CREQE6tati7p16yIiIgK2trYYPHiwwWosjEIUlVCVXPXq1bF8+XKEhIQUuPz8+fNo1aoVcnJySrRfhrh0iT+dwagRw/PN792nL+ZGzDdCRaalSpt3jF2CLEWFD0bntvXhVs0RDx5n4eLVP/DZ+u9x/Mz/tOsM79MOH7wViOouL+HKjXTMW/kd9sdeMGLV8vfkXMGfs9bHT9cf6L1t29pOJVo/NjYWnTt3zjc/NDQUGzZsgBACc+bMwapVq/DXX3/Bx8cHy5cvR5MmTfSusawwxIvQu3dvtGjRAp988kmBy3/55Re0bNkSubm5JdovQ5wMhSFOhmSqIW7K2J1ehA8++ACZmZmFLq9Tpw5OnDhhwIqIiCoHmdwSlz2GeBE6duxY5HI7Ozv4+/PbxIiIyhxTXBKGOBERyQ7/ipk0DHEiIpIdGXyPiklgiBMRkewww6XhN7YRERGZKLbEiYhIftgUl4QhTkREssOBbdIwxImISHY4sE0ahjgREckOM1wahjgREckPU1wSjk4nIiIyUWyJExGR7HBgmzQMcSIikh0ObJOGIU5ERLLDDJeGIU5ERPLDFJeEIU5ERLLDe+LScHQ6ERGRiWJLnIiIZIcD26RhiBMRkewww6VhiBMRkfwwxSVhiBMRkexwYJs0DHEiIpId3hOXhqPTiYiITBRb4kREJDtsiEvDECciIvlhikvCECciItnhwDZpGOJERCQ7HNgmDUOciIhkhxkuDUenExERmSi2xImISH7YFJeEIU5ERLLDgW3SMMSJiEh2OLBNGt4TJyIi2VGUYiqJ2bNnQ6FQ6Exubm5ldBbljy1xIiKSHwO2xBs3bozvv/9e+7O5ubnhDl5KDHEiIqrULCwsTKr1/Tx2pxMRkewoSvFPo9Hg4cOHOpNGoyn0WFevXoWHhwe8vLzw5ptv4vr16wY809JhiBMRkewoFPpPKpUKTk5OOpNKpSrwOD4+Pti0aRMOHz6MNWvWQK1Ww8/PDxkZGQY+Y/0ohBDC2EVUNlnPjF0BVRZV2rxj7BKoEnlyblmZ7evmvcJbzsVxsUO+lrdSqYRSqSx228zMTHh7e+PDDz/E5MmT9a7BUHhPnIiIZKc0HzGTGtgFsbOzQ9OmTXH16lX9CzAgdqcTEZEMGepDZro0Gg0uX74Md3f3Uu3HUBjiRERUaU2dOhVxcXFITk7GmTNnMGDAADx8+BChoaHGLk0SdqcTEZHsGOob227duoVBgwbh7t27ePnll9GuXTskJCTA09PTMAWUEkOciIhkx1Df9RITE2OgI5UPhjgREckOvztdGoY4ERHJDv+KmTQMcSIikh9muCQcnU5ERGSi2BInIiLZYUNcGoY4ERHJDge2ScMQJyIi2eHANmkY4kREJD/McEkY4kREJDvMcGk4Op2IiMhEsSVORESyw4Ft0jDEiYhIdjiwTRqGOBERyQ5b4tLwnjgREZGJYkuciIhkhy1xadgSJyIiMlFsiRMRkexwYJs0DHEiIpIddqdLwxAnIiLZYYZLwxAnIiL5YYpLwoFtREREJootcSIikh0ObJOGIU5ERLLDgW3SMMSJiEh2mOHSMMSJiEh+mOKSMMSJiEh2eE9cGo5OJyIiMlFsiRMRkexwYJs0CiGEMHYRRMXRaDRQqVSYPn06lEqlscuhCoyPNTIlDHEyCQ8fPoSTkxMePHgAR0dHY5dDFRgfa2RKeE+ciIjIRDHEiYiITBRDnIiIyEQxxMkkKJVKhIeHc6ARlTs+1siUcGAbERGRiWJLnIiIyEQxxImIiEwUQ5yIiMhEMcSJiIhMFEOcZG/FihXw8vKCtbU1WrVqhR9++MHYJVEFdPLkSQQHB8PDwwMKhQJ79uwxdklExWKIk6xt374dkyZNwsyZM3Hu3Dl07NgRQUFBSE1NNXZpVMFkZmaiefPmWLZsmbFLIZKMHzEjWfPx8cGrr76KqKgo7byGDRsiJCQEKpXKiJVRRaZQKLB7926EhIQYuxSiIrElTrL19OlT/PzzzwgMDNSZHxgYiNOnTxupKiIi+WCIk2zdvXsXOTk5cHV11Znv6uoKtVptpKqIiOSDIU6yp1AodH4WQuSbR0RUGTHESbaqVasGc3PzfK3u9PT0fK1zIqLKiCFOsmVlZYVWrVrh6NGjOvOPHj0KPz8/I1VFRCQfFsYugKgokydPxrBhw9C6dWv4+vpi9erVSE1NxdixY41dGlUwjx8/xu+//679OTk5GefPn4ezszNq1aplxMqICsePmJHsrVixApGRkUhLS0OTJk2wePFidOrUydhlUQUTGxuLzp0755sfGhqKDRs2GL4gIgkY4kRERCaK98SJiIhMFEOciIjIRDHEiYiITBRDnIiIyEQxxImIiEwUQ5yIiMhEMcSJiIhMFEOciIjIRDHEiSqI2bNno0WLFtqfw8LCEBISYvA6UlJSoFAocP78eYMfm6iyYYgTlbOwsDAoFAooFApYWlqidu3amDp1KjIzM8v1uEuWLJH8daEMXiLTxD+AQmQA3bt3x/r165GdnY0ffvgBo0aNQmZmJqKionTWy87OhqWlZZkc08nJqUz2Q0TyxZY4kQEolUq4ubmhZs2aGDx4MIYMGYI9e/Zou8Cjo6NRu3ZtKJVKCCHw4MEDvP3223BxcYGjoyO6dOmCX375RWef8+fPh6urKxwcHDBy5EhkZWXpLH+xOz03NxcLFixAnTp1oFQqUatWLXz66acAAC8vLwBAy5YtoVAoEBAQoN1u/fr1aNiwIaytrdGgQQOsWLFC5zg//fQTWrZsCWtra7Ru3Rrnzp0rwytHREVhS5zICGxsbJCdnQ0A+P3337Fjxw7s3LkT5ubmAICePXvC2dkZBw4cgJOTE1atWoWuXbviypUrcHZ2xo4dOxAeHo7ly5ejY8eO+Oqrr/Dll1+idu3ahR5z+vTpWLNmDRYvXowOHTogLS0N//vf/wD8E8Rt27bF999/j8aNG8PKygoAsGbNGoSHh2PZsmVo2bIlzp07h9GjR8POzg6hoaHIzMxEr1690KVLF2zevBnJycl47733yvnqEZGWIKJyFRoaKvr06aP9+cyZM6Jq1arijTfeEOHh4cLS0lKkp6drlx87dkw4OjqKrKwsnf14e3uLVatWCSGE8PX1FWPHjtVZ7uPjI5o3b17gcR8+fCiUSqVYs2ZNgTUmJycLAOLcuXM682vWrCm2bt2qM2/u3LnC19dXCCHEqlWrhLOzs8jMzNQuj4qKKnBfRFT22J1OZAD79++Hvb09rK2t4evri06dOmHp0qUAAE9PT7z88svadX/++Wc8fvwYVatWhb29vXZKTk7GtWvXAACXL1+Gr6+vzjFe/Pl5ly9fhkajQdeuXSXXfOfOHdy8eRMjR47UqWPevHk6dTRv3hy2traS6iCissXudCID6Ny5M6KiomBpaQkPDw+dwWt2dnY66+bm5sLd3R2xsbH59vPSSy/pdXwbG5sSb5Obmwvgny51Hx8fnWV53f5CCL3qIaKywRAnMgA7OzvUqVNH0rqvvvoq1Go1LCws8MorrxS4TsOGDZGQkIDhw4dr5yUkJBS6z7p168LGxgbHjh3DqFGj8i3Puweek5Ojnefq6orq1avj+vXrGDJkSIH7bdSoEb766is8efJE+0ahqDqIqGyxO51IZl577TX4+voiJCQEhw8fRkpKCk6fPo2PPvoIZ8+eBQC89957iI6ORnR0NK5cuYLw8HAkJSUVuk9ra2tMmzYNH374ITZt2oRr164hISEB69atAwC4uLjAxsYGhw4dwu3bt/HgwQMA/3yBjEqlwpIlS3DlyhVcuHAB69evx+effw4AGDx4MMzMzDBy5EhcunQJBw4cwKJFi8r5ChFRHoY4kcwoFAocOHAAnTp1wltvvYV69erhzTffREpKClxdXQEAAwcOxKxZszBt2jS0atUKN27cwLhx44rc78cff4wpU6Zg1qxZaNiwIQYOHIj09HQAgIWFBb788kusWrUKHh4e6NOnDwBg1KhRWLt2LTZs2ICmTZvC398fGzZs0H4kzd7eHvv27cOlS5fQsmVLzJw5EwsWLCjHq0NEz1MI3tQiIiIySWyJExERmSiGOBERkYliiBMREZkohjgREZGJYogTERGZKIY4ERGRiWKIExERmSiGOBERkYliiBMREZkohjgREZGJYogTERGZqP8H1F3UmXQR5dIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1. Logistic Regression - No Regularization\n",
+    "evaluate_model(LogisticRegression(penalty=None, solver='saga', max_iter=10000, random_state=42),\n",
+    "               \"Logistic Regression (no regularization)\",\n",
+    "               X_train, y_train, X_test, y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0221a40",
+   "metadata": {},
+   "source": [
+    "### Regularized Logistic Regression - with L2 Penalty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "10dd34c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\gunav\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Metrics for Logistic Regression (L2 weighted):\n",
+      "accuracy: 0.8250\n",
+      "precision: 1.0000\n",
+      "recall: 0.8108\n",
+      "f1_score: 0.8955\n",
+      "roc_auc: 0.9189\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 2. Logistic Regression - L2 Regularization (weighted)\n",
+    "evaluate_model(LogisticRegression(penalty='l2', class_weight='balanced', solver='saga', random_state=42),\n",
+    "               \"Logistic Regression (L2 weighted)\",\n",
+    "               X_train, y_train, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02c39a4",
+   "metadata": {},
+   "source": [
+    "### Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cf790c08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Metrics for Decision Tree:\n",
+      "accuracy: 0.9250\n",
+      "precision: 0.9722\n",
+      "recall: 0.9459\n",
+      "f1_score: 0.9589\n",
+      "roc_auc: 0.8063\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3. Decision Tree\n",
+    "evaluate_model(DecisionTreeClassifier(class_weight='balanced', random_state=42),\n",
+    "               \"Decision Tree\",\n",
+    "               X_train, y_train, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bf4e052",
+   "metadata": {},
+   "source": [
+    "### Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8f03cf01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Metrics for Random Forest:\n",
+      "accuracy: 0.9250\n",
+      "precision: 0.9250\n",
+      "recall: 1.0000\n",
+      "f1_score: 0.9610\n",
+      "roc_auc: 0.9640\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 4. Random Forest\n",
+    "evaluate_model(RandomForestClassifier(n_estimators=300, max_depth=8, class_weight='balanced', random_state=42),\n",
+    "               \"Random Forest\",\n",
+    "               X_train, y_train, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "beaa9249",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Metrics for XGBoost:\n",
+      "accuracy: 0.9250\n",
+      "precision: 0.9250\n",
+      "recall: 1.0000\n",
+      "f1_score: 0.9610\n",
+      "roc_auc: 0.9189\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4+fmJuXPnVvo2hoULF4r27dsLKysr4ezsLF555ZVqP2f2oIiICO3noCqgitmMQgjx448/il69eolGjRqJ5s2bi5iYGLFu3TqdWWCHDx8Ww4YNE56enkKlUgknJycRFBQkdu7cWekcVX3OLCwsTDg4OAhra2vh6+sr4uPjddpUzPr797//rbM+KytLAKjU/kH3z2Z8mKpmJK5fv1506NBBqFQq0aZNG6FWq7Uf+r1/Flx2drYICQkR9vb2VX7O7MHa799WMZtx//79wsLCotJrlJ+fL1q1aiWefPJJodFoHnoN+igvLxeBgYHCzc1N5OfnV9peWloqfH19haenpygqKtLZtnPnThEWFiZcXV2FpaWlsLe3F926dRNvv/12pc/8PTib0cLCQnh4eIjQ0FCRmppa6bwVnzNr1KiRsLW1FT179hRff/11pXb6vGc+/vhj0atXL+Hs7Cysra1Fq1atxNixY0V2drZOu+joaOHh4SEsLCz4OTOJUAih59QnIiKiBor3zIiIyOwxzIiIyOwxzIiIyOwxzIiIyOwxzIiIyOwxzIiIyOwxzIiIyOxJ8htA7tw1dQUkFwW3y0xdAsmEWxMrox7P1u+NGu9bcny5ESsxDkmGGRERPYJCWgNzDDMiIjky4IkO5oBhRkQkRxLrmUnraoiISJbYMyMikiMOMxIRkdmT2DAjw4yISI7YMyMiIrPHnhkREZk9ifXMpBXNREQkS+yZERHJkcSGGaV1NUREpB+FouaLAeLi4tC1a1c0adIETZo0QWBgIPbs2aPdHhkZCYVCobP07NnT4Mthz4yISI7qqWfWokULLFiwAO3atQMAbNiwAUOHDsXx48fRuXNnAMDAgQMRHx+v3cfa2trg8zDMiIjkqJ4mgISFhen8/MEHHyAuLg4ZGRnaMFOpVHBzc6vVeTjMSEQkRwqLGi8ajQZFRUU6i0ajeeQpy8vLkZiYiOLiYgQGBmrXp6amwsXFBe3bt8f48eORl5dn8OUwzIiIyCBqtRoODg46i1qtrrb9qVOn0LhxY6hUKkyaNAnbt29Hp06dAAChoaHYvHkzUlJS8PHHHyMzMxP9+vXTKxzvpxBCiFpdVQPEh3NSfeHDOam+GP3hnEHzarxvwf5ZlcJGpVJBpVJV2b60tBSXLl1CQUEBtm3bhnXr1iEtLU0baPfLycmBp6cnEhMTMXz4cL1r4j0zIiI5sqj5PbOHBVdVrK2ttRNAunfvjszMTCxduhSrV6+u1Nbd3R2enp64cOGCQTUxzIiI5MiEnzMTQlQ7jJifn4/Lly/D3d3doGMyzIiI5KieZjPOnj0boaGhaNmyJW7evInExESkpqZi7969uHXrFmJjY/H888/D3d0d2dnZmD17NpydnTFs2DCDzsMwIyKSo3rqmf31118YM2YMcnJy4ODggK5du2Lv3r0YMGAASkpKcOrUKWzcuBEFBQVwd3dH3759kZSUBHt7e4POwwkgRLXACSBUX4w+AeSZBTXet+Tbd4xYiXGwZ0ZEJEcS+9Z8hhkRkRxJ7IuGGWZERHLEnhkREZk99syIiMjsSaxnJq1oJiIiWWLPjIhIjjjMSEREZk9iw4wMMyIiOWLPjIiIzB7DjIiIzJ7EhhmlFc1ERCRL7JkREckRhxmJiMjsSWyYkWFGRCRH7JkREZHZY8+MiIjMnUJiYSatfiYREckSe2ZERDIktZ4Zw4yISI6klWUMMyIiOWLPjIiIzB7DjIiIzJ7UwoyzGYmIyOyxZ0ZEJENS65kxzIiI5EhaWcYwIyKSI/bMiIjI7DHMiIjI7EktzDibkYiIzB57ZkREMsSeGRERmT9FLRYDxMXFoWvXrmjSpAmaNGmCwMBA7NmzR7tdCIHY2Fh4eHjA1tYWwcHBOHPmjMGXwzAjIpIhhUJR48UQLVq0wIIFC3D06FEcPXoU/fr1w9ChQ7WBtWjRInzyySdYvnw5MjMz4ebmhgEDBuDmzZuGXY8QQhi0hxm4c9fUFZBcFNwuM3UJJBNuTayMerxmrybVeN+r8SNrdW5HR0d8+OGHeO211+Dh4YGoqCjMmjULAKDRaODq6oqFCxdi4sSJeh+TPTMiIhmqTc9Mo9GgqKhIZ9FoNI88Z3l5ORITE1FcXIzAwEBkZWUhNzcXISEh2jYqlQpBQUFIT0836HoYZkREZBC1Wg0HBwedRa1WV9v+1KlTaNy4MVQqFSZNmoTt27ejU6dOyM3NBQC4urrqtHd1ddVu0xdnMxIRyVEtJjNGR0dj2rRpOutUKlW17Tt06IATJ06goKAA27ZtQ0REBNLS0v5XygP34YQQBt+bY5gREclQbabmq1Sqh4bXg6ytrdGuXTsAQPfu3ZGZmYmlS5dq75Pl5ubC3d1d2z4vL69Sb+1ROMxIRCRD9TWbsSpCCGg0Gnh5ecHNzQ3JycnabaWlpUhLS0OvXr0MOiZ7ZkREMlRfH5qePXs2QkND0bJlS9y8eROJiYlITU3F3r17oVAoEBUVhfnz58Pb2xve3t6YP38+7OzsMHr0aIPOwzAjIpKh+gqzv/76C2PGjEFOTg4cHBzQtWtX7N27FwMGDAAAzJw5EyUlJZg8eTJu3LiBgIAA7N+/H/b29gadh58zI6oFfs6M6ouxP2fmMfGrGu/75+rhRqzEONgzIyKSI2l9NSPDjIhIjqT2RcMMMyIiGWKYERGR2ZNamPFzZkREZPbYMyMikiNpdcwYZgQkfbEZCfGf4drVq2jbzhsz35mNJ/y7m7oskpAdWxPxn21JyM35EwDQuk07RIydhJ5P9TZxZfLFYUaSlL17dmPRAjXGT3gdSVt34Ikn/DF54njk/PmnqUsjCWnm4oaJb0zFmg1JWLMhCU9074E5099E1u+/mbo02TLl11nVBYaZzH2+IR7Dnn8ew0e8gDZt22Jm9By4ubvhy6QvTF0aSchTfYLR86k+aOnZGi09W2P85Ldga2eHs6dPmro02WKYkWSUlZbi3NkzCOz1tM76wF5P4eSJ4yaqiqSuvLwcB/bvxp2SEnTu0s3U5ciW1MLMpPfMrly5gri4OKSnpyM3NxcKhQKurq7o1asXJk2ahJYtW5qyPMm7UXAD5eXlcHJy0lnv5OSMa9eumqgqkqrff/sVU157GaWlpbC1tcO/PlyK1m3amroskgiThdn333+v/SblkJAQhISEQAiBvLw87NixA8uWLcOePXvw1FNPPfQ4Go2m0uO6hdKwZ+3InTEejEf0KK08vbBu8zbculmEQynJmB87B5+uTmCgmYrE/hc3WZhNnToV48aNw+LFi6vdHhUVhczMzIceR61W47333tNZN+fdGPzf3FhjlSpZTR9rCqVSiWvXrumsv349H05OziaqiqTKysoKLVq2AgA83skH58+ewdbETZg+O8bElcmT1P7BarJ7ZqdPn8akSZOq3T5x4kScPn36kceJjo5GYWGhzjJjVrQxS5UsK2trdOzUGRnpP+isz0hPh283PxNVRXIhhEBZaampy5At3jMzEnd3d6Snp6NDhw5Vbj98+LDOY7SrU9Xju/kIGP2NiXgVc96ZiU4+PvD19cO2fychJycHL4wcZerSSELWrFiCgF694eLqhtu3i5Gyfw9O/JSJRZ+uMnVpstVAM6nGTBZm06dPx6RJk3Ds2DEMGDAArq6uUCgUyM3NRXJyMtatW4clS5aYqjzZGBg6CIUFN7AmbiWuXs1DO+/2WLFqDTw8mpu6NJKQG9fzMT8mGvnXrqJRY3u0bdceiz5dhScDepm6NNlqqD2smjLpwzmTkpKwePFiHDt2DOXl5QAApVIJf39/TJs2DS+++GKNjsueGdUXPpyT6ouxH87pPWNvjfe98OFAI1ZiHCadmj9y5EiMHDkSZWVl2kkIzs7OsLIy7l8aERHpkljHrGF8N6OVlZVe98eIiMg4pDbM2CDCjIiI6pfEsoxhRkQkRxYW0kozhhkRkQxJrWfGLxomIiKzx54ZEZEMcQIIERGZPYllGcOMiEiO2DMjIiKzxzAjIiKzJ7Es42xGIiIyf+yZERHJEIcZiYjI7EksyzjMSEQkR/X1pGm1Wo0nn3wS9vb2cHFxQXh4OH755RedNpGRkZXO0bNnT4POwzAjIpIhhaLmiyHS0tIwZcoUZGRkIDk5GXfv3kVISAiKi4t12g0cOBA5OTnaZffu3Qadh8OMREQyVF/3zPbu1X0IaHx8PFxcXHDs2DH06dNHu16lUsHNza3G52HPjIiIDKLRaFBUVKSzaDQavfYtLCwEADg6OuqsT01NhYuLC9q3b4/x48cjLy/PoJoYZkREMlSbYUa1Wg0HBwedRa1WP/KcQghMmzYNTz/9NHx8fLTrQ0NDsXnzZqSkpODjjz9GZmYm+vXrp3dAAoBCCCFq9Eo0YHfumroCkouC22WmLoFkwq2JlVGPF6BOq/G+h6b1rBQ0KpUKKpXqoftNmTIF33zzDb7//nu0aNGi2nY5OTnw9PREYmIihg8frldNvGdGRCRDtbllpk9wPejNN9/Ezp07cejQoYcGGQC4u7vD09MTFy5c0Pv4DDMiIhmqrwkgQgi8+eab2L59O1JTU+Hl5fXIffLz83H58mW4u7vrfR7eMyMikqH6mpo/ZcoUbNq0CVu2bIG9vT1yc3ORm5uLkpISAMCtW7cwffp0HD58GNnZ2UhNTUVYWBicnZ0xbNgwvc/DnhkREdWZuLg4AEBwcLDO+vj4eERGRkKpVOLUqVPYuHEjCgoK4O7ujr59+yIpKQn29vZ6n4dhRkQkQ/U5zPgwtra22LdvX63PwzAjIpIhqX03I8OMiEiG+K35RERk9hhmRERk9iSWZZyaT0RE5o89MyIiGeIwIxERmT2JZRnDjIhIjtgzIyIisyexLGOYERHJkYXE0oyzGYmIyOyxZ0ZEJEMS65gxzIiI5IgTQIiIyOxZSCvLGGZERHLEnhkREZk9iWUZZzMSEZH5Y8+MiEiGFJBW14xhRkQkQ5wAQkREZo8TQIiIyOxJLMsYZkREcsTvZiQiImpg2DMjIpIhiXXMGGZERHLECSBERGT2JJZlDDMiIjmS2gQQhhkRkQxJK8r0DLOdO3fqfcAhQ4bUuBgiIqKa0CvMwsPD9TqYQqFAeXl5beohIqJ6ILUJIHp9zuzevXt6LQwyIiLzYKGo+WIItVqNJ598Evb29nBxcUF4eDh++eUXnTZCCMTGxsLDwwO2trYIDg7GmTNnDLsew8oiIiIpUCgUNV4MkZaWhilTpiAjIwPJycm4e/cuQkJCUFxcrG2zaNEifPLJJ1i+fDkyMzPh5uaGAQMG4ObNm/pfjxBCGFQZgOLiYqSlpeHSpUsoLS3V2fbPf/7T0MMZ3Z27pq6A5KLgdpmpSyCZcGtiZdTjjdl8ssb7fv6yb433vXr1KlxcXJCWloY+ffpACAEPDw9ERUVh1qxZAACNRgNXV1csXLgQEydO1Ou4Bs9mPH78OAYNGoTbt2+juLgYjo6OuHbtGuzs7ODi4tIgwoyIiB6uNvfMNBoNNBqNzjqVSgWVSvXIfQsLCwEAjo6OAICsrCzk5uYiJCRE51hBQUFIT0/XO8wMHmacOnUqwsLCcP36ddja2iIjIwN//PEH/P398dFHHxl6OCIiMjNqtRoODg46i1qtfuR+QghMmzYNTz/9NHx8fAAAubm5AABXV1edtq6urtpt+jC4Z3bixAmsXr0aSqUSSqUSGo0Gbdq0waJFixAREYHhw4cbekgiIqpntXk4Z3R0NKZNm6azTp9e2RtvvIGff/4Z33//faVtD/YUhRAG9R4NDjMrKyvtCVxdXXHp0iV07NgRDg4OuHTpkqGHIyIiE6jNMKO+Q4r3e/PNN7Fz504cOnQILVq00K53c3MD8HcPzd3dXbs+Ly+vUm/tYQweZvTz88PRo0cBAH379sXcuXOxefNmREVFoUuXLoYejoiITEBRi8UQQgi88cYb+Oqrr5CSkgIvLy+d7V5eXnBzc0NycrJ2XWlpKdLS0tCrVy+9z2NwmM2fP1+bnu+//z6cnJzw+uuvIy8vD2vWrDH0cEREZAIWCkWNF0NMmTIFmzZtwpYtW2Bvb4/c3Fzk5uaipKQEwN89xKioKMyfPx/bt2/H6dOnERkZCTs7O4wePVrv89Roan5Dx6n5VF84NZ/qi7Gn5o9LOl3jfdeN9NG7bXXDmfHx8YiMjATwd+/tvffew+rVq3Hjxg0EBARgxYoV2kkiep2HYUZUcwwzqi/GDrPxX9Y8zNa+qH/I1BeDJ4B4eXk99MbhxYsXa1UQERHVPal9N6PBYRYVFaXzc1lZGY4fP469e/dixowZxqqLiIjqkMSyzPAwe+utt6pcv2LFCu0sRyIiatik9nBOo33RcGhoKLZt22aswxERUR1SKGq+NERGC7OtW7dqv2uLiIioPhk8zOjn56dz41AIgdzcXFy9ehUrV640anFERFQ3ZD8BZOjQoTovgoWFBZo1a4bg4GA8/vjjRi2OqKHzCppq6hJIJkqOLzfq8aT2MEuDwyw2NrYOyiAiovoktZ6ZweGsVCqRl5dXaX1+fj6USqVRiiIiorploaj50hAZ3DOr7gtDNBoNrK2ta10QERHVvYYaSjWld5h9+umnAP7umq5btw6NGzfWbisvL8ehQ4d4z4yIiExC7zBbvHgxgL97ZqtWrdIZUrS2tkbr1q2xatUq41dIRERGJ7V7ZnqHWVZWFoC/n2H21VdfoWnTpnVWFBER1S3ZDjNWOHjwYF3UQURE9UhiHTPDZzOOGDECCxYsqLT+ww8/xAsvvGCUooiIqG7V18M564vBYZaWlobBgwdXWj9w4EAcOnTIKEUREVHdsqjF0hAZXNetW7eqnIJvZWWFoqIioxRFRERkCIPDzMfHB0lJSZXWJyYmolOnTkYpioiI6pbUvjXf4Akg7777Lp5//nn8/vvv6NevHwDgwIED2LJlC7Zu3Wr0AomIyPga6r2vmjI4zIYMGYIdO3Zg/vz52Lp1K2xtbeHr64uUlBQ0adKkLmokIiIjk1iWGR5mADB48GDtJJCCggJs3rwZUVFROHnyJMrLy41aIBERGZ/UPmdW44kpKSkpeOWVV+Dh4YHly5dj0KBBOHr0qDFrIyKiOiK1qfkG9cyuXLmChIQErF+/HsXFxXjxxRdRVlaGbdu2cfIHERGZjN49s0GDBqFTp044e/Ysli1bhj///BPLli2ry9qIiKiOyHY24/79+/HPf/4Tr7/+Ory9veuyJiIiqmOyvWf23Xff4ebNm+jevTsCAgKwfPlyXL16tS5rIyKiOqKoxZ+GSO8wCwwMxNq1a5GTk4OJEyciMTERzZs3x71795CcnIybN2/WZZ1ERGREUnvStMGzGe3s7PDaa6/h+++/x6lTp/D2229jwYIFcHFxwZAhQ+qiRiIiMjLZh9n9OnTogEWLFuHKlSv44osvjFUTERGRQWr0oekHKZVKhIeHIzw83BiHIyKiOibbJ00TEZF0NNThwppqqI+mISKiOlRfnzM7dOgQwsLC4OHhAYVCgR07duhsj4yMhEKh0Fl69uxp8PUwzIiIZKi+vs6quLgYvr6+WL58ebVtBg4ciJycHO2ye/dug6+Hw4xERDJUX8OMoaGhCA0NfWgblUoFNze3Wp2HPTMiIjKIRqNBUVGRzqLRaGp8vNTUVLi4uKB9+/YYP3488vLyDD4Gw4yISIZqc89MrVbDwcFBZ1Gr1TWqIzQ0FJs3b0ZKSgo+/vhjZGZmol+/fgaHI4cZiYhkyKIWX0sVHR2NadOm6axTqVQ1OtbIkSO1/+3j44Pu3bvD09MT33zzDYYPH673cRhmREQyVJuPmalUqhqH16O4u7vD09MTFy5cMGg/hhkRkQw11M+Z5efn4/Lly3B3dzdoP4YZEZEM1dcTo2/duoXffvtN+3NWVhZOnDgBR0dHODo6IjY2Fs8//zzc3d2RnZ2N2bNnw9nZGcOGDTPoPAwzIiKqM0ePHkXfvn21P1fca4uIiEBcXBxOnTqFjRs3oqCgAO7u7ujbty+SkpJgb29v0HkYZkREMlRfX80YHBwMIUS12/ft22eU8zDMiIhkqL6GGesLw4yISIYklmUMMyIiOZLaN2YwzIiIZEhqzzOTWjgTEZEMsWdGRCRD0uqXMcyIiGSJsxmJiMjsSSvKGGZERLIksY4Zw4yISI44m5GIiKiBYc+MiEiGpNaTYZgREcmQ1IYZGWZERDIkrShjmBERyRJ7ZkREZPakds9MatdDREQyxJ4ZEZEMcZiRiIjMnrSijGFGRCRLEuuYMcyIiOTIQmJ9M4YZEZEMSa1nxtmMRERk9tgzIyKSIQWHGYmIyNxJbZiRYUZEJEOcAEJERGaPPTMiIjJ7UgszzmYkIiKzx54ZEZEMcTYjERGZPQtpZRmHGYmI5EhRiz+GOHToEMLCwuDh4QGFQoEdO3bobBdCIDY2Fh4eHrC1tUVwcDDOnDlj8PUwzIiIZEihqPliiOLiYvj6+mL58uVVbl+0aBE++eQTLF++HJmZmXBzc8OAAQNw8+ZNg87DYUYiIjKIRqOBRqPRWadSqaBSqSq1DQ0NRWhoaJXHEUJgyZIlmDNnDoYPHw4A2LBhA1xdXbFlyxZMnDhR75rYMyMikqHaDDOq1Wo4ODjoLGq12uAasrKykJubi5CQEO06lUqFoKAgpKenG3Qs9swISV9sRkL8Z7h29SratvPGzHdm4wn/7qYui8zY+BeexvgRveHp4QgAOHcxF/PX7MH+H84CAEqOVz3kNHvxdizeeKDe6pSz2kwAiY6OxrRp03TWVdUre5Tc3FwAgKurq856V1dX/PHHHwYdi2Emc3v37MaiBWrMeTcG3fyewNYvEzF54nhs3/kN3D08TF0eman//lWAd5f9B79fugYAeCUsAP9ePAE9Ry3AuYu5aP1MtE77kKc6Y1XMaGw/cMIE1cpTbabmVzekWONaHrgRJ4SotO5ROMwoc59viMew55/H8BEvoE3btpgZPQdu7m74MukLU5dGZmz3odPY9/1Z/HYpD79dykPsiq9x67YGPbp6AQD+yr+ps4QFd0Fa5gVk/zffxJXLR31NAHkYNzc3AP/roVXIy8ur1Ft7FIaZjJWVluLc2TMI7PW0zvrAXk/h5InjJqqKpMbCQoEXnvVHI1trHPk5q9J2F0d7DHzaBxt2HDZBdfKlqMViLF5eXnBzc0NycrJ2XWlpKdLS0tCrVy+DjsVhRhm7UXAD5eXlcHJy0lnv5OSMa9eumqgqkorO7TyQuuFt2Fhb4laJBiPfXovzF3MrtXslLAA3b9/BjpQT9V8k1blbt27ht99+0/6clZWFEydOwNHREa1atUJUVBTmz58Pb29veHt7Y/78+bCzs8Po0aMNOk+DDrPLly8jJiYG69evr7ZNVVNEhdK447lSZ4zxaqIH/Zr9FwJGqfGYvR3C+3fD2nljEDJuaaVA+8fQnkjacxSa0rsmqlSeLOrp//GjR4+ib9++2p8rJo5EREQgISEBM2fORElJCSZPnowbN24gICAA+/fvh729vUHnadDDjNevX8eGDRse2qaqKaIfLjR8iqgcNX2sKZRKJa5du6az/vr1fDg5OZuoKpKKsrvluHj5Gn46ewlzl+3EqV//iykvBeu0ecqvLTp4uSF+u2HTsKn26muYMTg4GEKISktCQsLfdSgUiI2NRU5ODu7cuYO0tDT4+PgYfD0m7Znt3LnzodsvXrz4yGNUNUVUKNkr04eVtTU6duqMjPQf0P+ZAdr1GenpCO7X34SVkRQpoIDKWvdXTkR4II6dvYRTv/7XRFXJmMQGX0waZuHh4VAoFBBCVNvmUcNdVU0RvcPRCr2NiXgVc96ZiU4+PvD19cO2fychJycHL4wcZerSyIy990YY9v9wFpdzb8C+kQ1eeNYffbp7Y8iUldo29o1sMHyAH975ZLsJK5Uvfmu+Ebm7u2PFihUIDw+vcvuJEyfg7+9fv0XJzMDQQSgsuIE1cStx9Woe2nm3x4pVa+Dh0dzUpZEZc3Gyx2f/+gfcnJug8NYdnL7wXwyZshIpR85r27zwrD8UUODLvUdNWKl8Se22uEI8rFtUx4YMGYJu3bph3rx5VW4/efIk/Pz8cO/ePYOOy54Z1ZemT75h6hJIJqr71pSa+vFiYY337dHGwYiVGIdJe2YzZsxAcXFxtdvbtWuHgwcP1mNFRETyILGOmWnDrHfv3g/d3qhRIwQFBdVTNUREMiKxNGvQnzMjIqK6wQkgRERk9qQ2AYRhRkQkQxLLsob9DSBERET6YM+MiEiOJNY1Y5gREckQJ4AQEZHZ4wQQIiIyexLLMoYZEZEsSSzNOJuRiIjMHntmREQyxAkgRERk9jgBhIiIzJ7EsoxhRkQkSxJLM4YZEZEMSe2eGWczEhGR2WPPjIhIhjgBhIiIzJ7EsoxhRkQkSxJLM4YZEZEMSW0CCMOMiEiGpHbPjLMZiYjI7LFnRkQkQxLrmDHMiIhkSWJpxmFGIiIZUtTijyFiY2OhUCh0Fjc3N6NfD3tmREQyVJ8TQDp37oxvv/1W+7NSqTT6ORhmREQyVJ+jjJaWlnXSG7sfhxmJiMggGo0GRUVFOotGo6m2/YULF+Dh4QEvLy+MGjUKFy9eNHpNDDMiIjlS1HxRq9VwcHDQWdRqdZWnCQgIwMaNG7Fv3z6sXbsWubm56NWrF/Lz8417OUIIYdQjNgB37pq6ApKLpk++YeoSSCZKji836vEuXr1T432bN1FU6ompVCqoVKpH7ltcXIy2bdti5syZmDZtWo1reBDvmRERyVBtJoDoG1xVadSoEbp06YILFy7UvIAqcJiRiEiGajHKWCsajQbnzp2Du7t7LY+ki2FGRCRH9ZRm06dPR1paGrKysnDkyBGMGDECRUVFiIiIMNaVAOAwIxER1aErV67gpZdewrVr19CsWTP07NkTGRkZ8PT0NOp5GGZERDJUX4+ASUxMrJfzMMyIiGRIao+AYZgREcmQxLKMYUZEJEfsmRERkQRIK804NZ+IiMwee2ZERDLEYUYiIjJ7EssyhhkRkRyxZ0ZERGavvj40XV8YZkREciStLONsRiIiMn/smRERyZDEOmYMMyIiOeIEECIiMnucAEJEROZPWlnGMCMikiOJZRlnMxIRkfljz4yISIY4AYSIiMweJ4AQEZHZk1rPjPfMiIjI7LFnRkQkQ+yZERERNTDsmRERyRAngBARkdmT2jAjw4yISIYklmUMMyIiWZJYmnECCBERmT32zIiIZIgTQIiIyOxxAggREZk9iWUZ75kREcmSohZLDaxcuRJeXl6wsbGBv78/vvvuu9pegQ6GGRGRDClq8cdQSUlJiIqKwpw5c3D8+HH07t0boaGhuHTpkvGuRwghjHa0BuLOXVNXQHLR9Mk3TF0CyUTJ8eXGPV5Zzfe1tTKsfUBAAJ544gnExcVp13Xs2BHh4eFQq9U1L+Q+vGdGRCRDtZkAotFooNFodNapVCqoVKpKbUtLS3Hs2DG88847OutDQkKQnp5e8yIeIMkws5HkVdUtjUYDtVqN6OjoKt+QVDVj/2tZDvheaxhq83sy9l9qvPfeezrrYmJiEBsbW6nttWvXUF5eDldXV531rq6uyM3NrXkRD5DkMCMZrqioCA4ODigsLESTJk1MXQ5JGN9r5s+Qntmff/6J5s2bIz09HYGBgdr1H3zwAT7//HOcP3/eKDWxD0NERAapLriq4uzsDKVSWakXlpeXV6m3VhuczUhERHXG2toa/v7+SE5O1lmfnJyMXr16Ge087JkREVGdmjZtGsaMGYPu3bsjMDAQa9aswaVLlzBp0iSjnYNhRgD+HjaIiYnhDXmqc3yvyc/IkSORn5+PefPmIScnBz4+Pti9ezc8PT2Ndg5OACEiIrPHe2ZERGT2GGZERGT2GGZERGT2GGZERGT2GGZU549mIAKAQ4cOISwsDB4eHlAoFNixY4epSyIJYZjJXH08moEIAIqLi+Hr64vly/l9lmR8nJovc/XxaAaiBykUCmzfvh3h4eGmLoUkgj0zGat4NENISIjOemM/moGIqK4xzGSsvh7NQERU1xhmBMUDT+kTQlRaR0TUkDHMZKy+Hs1ARFTXGGYyVl+PZiAiqmv81nyZq49HMxABwK1bt/Dbb79pf87KysKJEyfg6OiIVq1ambAykgJOzSesXLkSixYt0j6aYfHixejTp4+pyyKJSU1NRd++fSutj4iIQEJCQv0XRJLCMCMiIrPHe2ZERGT2GGZERGT2GGZERGT2GGZERGT2GGZERGT2GGZERGT2GGZERGT2GGZERGT2GGZEeoqNjUW3bt20P0dGRprk4ZLZ2dlQKBQ4ceJEvZ+bqKFimJHZi4yMhEKhgEKhgJWVFdq0aYPp06ejuLi4Ts+7dOlSvb+GiQFEVLf4RcMkCQMHDkR8fDzKysrw3XffYdy4cSguLkZcXJxOu7KyMlhZWRnlnA4ODkY5DhHVHntmJAkqlQpubm5o2bIlRo8ejZdffhk7duzQDg2uX78ebdq0gUqlghAChYWFmDBhAlxcXNCkSRP069cPJ0+e1DnmggUL4OrqCnt7e4wdOxZ37tzR2f7gMOO9e/ewcOFCtGvXDiqVCq1atcIHH3wAAPDy8gIA+Pn5QaFQIDg4WLtffHw8OnbsCBsbGzz++ONYuXKlznl+/PFH+Pn5wcbGBt27d8fx48eN+MoRSQN7ZiRJtra2KCsrAwD89ttv+PLLL7Ft2zYolUoAwODBg+Ho6Ijdu3fDwcEBq1evRv/+/fHrr7/C0dERX375JWJiYrBixQr07t0bn3/+OT799FO0adOm2nNGR0dj7dq1WLx4MZ5++mnk5OTg/PnzAP4OpB49euDbb79F586dYW1tDQBYu3YtYmJisHz5cvj5+eH48eMYP348GjVqhIiICBQXF+O5555Dv379sGnTJmRlZeGtt96q41ePyAwJIjMXEREhhg4dqv35yJEjwsnJSbz44osiJiZGWFlZiby8PO32AwcOiCZNmog7d+7oHKdt27Zi9erVQgghAgMDxaRJk3S2BwQECF9f3yrPW1RUJFQqlVi7dm2VNWZlZQkA4vjx4zrrW7ZsKbZs2aKz7v333xeBgYFCCCFWr14tHB0dRXFxsXZ7XFxclccikjMOM5Ik7Nq1C40bN4aNjQ0CAwPRp08fLFu2DADg6emJZs2aadseO3YMt27dgpOTExo3bqxdsrKy8PvvvwMAzp07h8DAQJ1zPPjz/c6dOweNRoP+/fvrXfPVq1dx+fJljB07VqeOf/3rXzp1+Pr6ws7OTq86iOSKw4wkCX379kVcXBysrKzg4eGhM8mjUaNGOm3v3bsHd3d3pKamVjrOY489VqPz29raGrzPvXv3APw91BgQEKCzrWI4VPBxg0R6YZiRJDRq1Ajt2rXTq+0TTzyB3NxcWFpaonXr1lW26dixIzIyMvCPf/xDuy4jI6PaY3p7e8PW1hYHDhzAuHHjKm2vuEdWXl6uXefq6ormzZvj4sWLePnll6s8bqdOnfD555+jpKREG5gPq4NIrjjMSLLzzDPPIDAwEOHh4di3bx+ys7ORnp6O//u//8PRo0cBAG+99RbWr1+P9evX49dff0VMTAzOnDlT7TFtbGwwa9YszJw5Exs3bsTvv/+OjIwMfPbZZwAAFxcX2NraYu/evfjrr79QWFgI4O8PYqvVaixduhS//vorTp06hfj4eHzyyScAgNGjR8PCwgJjx47F2bNnsXv3bnz00Ud1/AoRmR+GGcmOQqHA7t270adPH7z22mto3749Ro0ahezsbLi6ugIARo4ciblz52LWrFnw9/fHH3/8gddff/2hx3333Xfx9ttvY+7cuejYsSNGjhyJvLw8AIClpSU+/fRTrF69Gh4eHhg6dCgAYNy4cVi3bh0SEhLQpUsXBAUFISEhQTuVv3Hjxvj6669x9uxZ+Pn5Yc6cOVi4cGEdvjpE5kkhOChPRERmjj0zIiIyewwzIiIyewwzIiIyewwzIiIyewwzIiIyewwzIiIyewwzIiIyewwzIiIyewwzIiIyewwzIiIyewwzIiIye/8PuQqVKFCAPs0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 5. XGBoost\n",
+    "evaluate_model(XGBClassifier(n_estimators=300, max_depth=8, learning_rate=0.05,\n",
+    "                             eval_metric='logloss', random_state=42),\n",
+    "               \"XGBoost\",\n",
+    "               X_train, y_train, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3b8b185d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Metrics for Neural Network SKLEARN:\n",
+      "accuracy: 0.9500\n",
+      "precision: 0.9730\n",
+      "recall: 0.9730\n",
+      "f1_score: 0.9730\n",
+      "roc_auc: 0.9459\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 6. Neural Network (MLP)\n",
+    "evaluate_model(MLPClassifier(hidden_layer_sizes=(64,32), max_iter=1000, random_state=42),\n",
+    "               \"Neural Network SKLEARN\",\n",
+    "               X_train, y_train, X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f1b18092",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\gunav\\anaconda3\\Lib\\site-packages\\keras\\src\\export\\tf2onnx_lib.py:8: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.\n",
+      "  if not hasattr(np, \"object\"):\n",
+      "c:\\Users\\gunav\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:106: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+      "Metrics for TensorFlow Neural Network (1 hidden layer):\n",
+      "Accuracy: 0.9250\n",
+      "Precision: 0.9250\n",
+      "Recall: 1.0000\n",
+      "F1-score: 0.9610\n",
+      "ROC-AUC: 0.9820\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, BatchNormalization, Dropout\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, precision_recall_curve, average_precision_score\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "# Assuming X_train, X_test, y_train, y_test are already prepared and scaled\n",
+    "\n",
+    "# 1. Build simple sequential network\n",
+    "model = Sequential([\n",
+    "    Dense(64, input_dim=X_train.shape[1], activation='relu'),  # One hidden layer with 64 units\n",
+    "    Dense(1, activation='sigmoid')  # Output layer for binary classification\n",
+    "])\n",
+    "\n",
+    "# 2. Compile model\n",
+    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# 3. Train model\n",
+    "history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1, verbose=0)\n",
+    "\n",
+    "# 4. Predict probabilities and class labels\n",
+    "y_proba = model.predict(X_test).flatten()\n",
+    "y_pred = (y_proba >= 0.5).astype(int)\n",
+    "\n",
+    "# 5. Calculate metrics\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "precision = precision_score(y_test, y_pred, zero_division=0)\n",
+    "recall = recall_score(y_test, y_pred, zero_division=0)\n",
+    "f1 = f1_score(y_test, y_pred, zero_division=0)\n",
+    "roc_auc = roc_auc_score(y_test, y_proba)\n",
+    "avg_precision = average_precision_score(y_test, y_proba)\n",
+    "\n",
+    "print(\"Metrics for TensorFlow Neural Network (1 hidden layer):\")\n",
+    "print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "print(f\"Precision: {precision:.4f}\")\n",
+    "print(f\"Recall: {recall:.4f}\")\n",
+    "print(f\"F1-score: {f1:.4f}\")\n",
+    "print(f\"ROC-AUC: {roc_auc:.4f}\")\n",
+    "\n",
+    "# 6. Confusion Matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "plt.figure(figsize=(5,4))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('Actual')\n",
+    "plt.title('Confusion Matrix - Neural Network')\n",
+    "plt.show()\n",
+    "\n",
+    "# 7. Precision-Recall curve vs Threshold\n",
+    "precision_vals, recall_vals, thresholds = precision_recall_curve(y_test, y_proba)\n",
+    "plt.figure(figsize=(7,5))\n",
+    "plt.plot(thresholds, precision_vals[:-1], label='Precision', color='blue')\n",
+    "plt.plot(thresholds, recall_vals[:-1], label='Recall', color='green')\n",
+    "plt.xlabel('Threshold')\n",
+    "plt.ylabel('Score')\n",
+    "plt.title('Precision and Recall vs Threshold - Neural Network')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28832691",
+   "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>Logistic Regression (no regularization)</th>\n",
+       "      <th>Logistic Regression (L2 weighted)</th>\n",
+       "      <th>Decision Tree</th>\n",
+       "      <th>Random Forest</th>\n",
+       "      <th>XGBoost</th>\n",
+       "      <th>Neural Network SKLEARN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.825</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>precision</th>\n",
+       "      <td>0.972973</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.972222</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.972973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall</th>\n",
+       "      <td>0.972973</td>\n",
+       "      <td>0.810811</td>\n",
+       "      <td>0.945946</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.972973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1_score</th>\n",
+       "      <td>0.972973</td>\n",
+       "      <td>0.895522</td>\n",
+       "      <td>0.958904</td>\n",
+       "      <td>0.961039</td>\n",
+       "      <td>0.961039</td>\n",
+       "      <td>0.972973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>roc_auc</th>\n",
+       "      <td>0.954955</td>\n",
+       "      <td>0.918919</td>\n",
+       "      <td>0.806306</td>\n",
+       "      <td>0.963964</td>\n",
+       "      <td>0.918919</td>\n",
+       "      <td>0.945946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pr_curve</th>\n",
+       "      <td>([0.925, 0.9230769230769231, 0.947368421052631...</td>\n",
+       "      <td>([0.925, 0.9230769230769231, 0.921052631578947...</td>\n",
+       "      <td>([0.925, 0.9722222222222222, 1.0], [1.0, 0.945...</td>\n",
+       "      <td>([0.925, 0.9230769230769231, 0.947368421052631...</td>\n",
+       "      <td>([0.925, 0.9230769230769231, 0.947368421052631...</td>\n",
+       "      <td>([0.925, 0.9487179487179487, 0.947368421052631...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Logistic Regression (no regularization)  ...                             Neural Network SKLEARN\n",
+       "accuracy                                                0.95  ...                                               0.95\n",
+       "precision                                           0.972973  ...                                           0.972973\n",
+       "recall                                              0.972973  ...                                           0.972973\n",
+       "f1_score                                            0.972973  ...                                           0.972973\n",
+       "roc_auc                                             0.954955  ...                                           0.945946\n",
+       "pr_curve   ([0.925, 0.9230769230769231, 0.947368421052631...  ...  ([0.925, 0.9487179487179487, 0.947368421052631...\n",
+       "\n",
+       "[6 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(model_metrics) # display metrics for all models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c37de2b",
+   "metadata": {},
+   "source": [
+    "## 4.3. Model Evaluation\n",
+    "\n",
+    "| Model                             | Accuracy | Precision | Recall | F1-score | ROC-AUC |\n",
+    "| --------------------------------- | -------- | --------- | ------ | -------- | ------- |\n",
+    "| Logistic Regression (no reg)      | 0.9500   | 0.9730    | 0.9730 | 0.9730   | 0.9550  |\n",
+    "| Logistic Regression (L2 weighted) | 0.8250   | 1.0000    | 0.8108 | 0.8955   | 0.9189  |\n",
+    "| Decision Tree                     | 0.9250   | 0.9722    | 0.9459 | 0.9589   | 0.8063  |\n",
+    "| Random Forest                     | 0.9250   | 0.9250    | 1.0000 | 0.9610   | 0.9640  |\n",
+    "| XGBoost                           | 0.9250   | 0.9250    | 1.0000 | 0.9610   | 0.9189  |\n",
+    "| Neural Network (sklearn MLP)      | 0.9500   | 0.9730    | 0.9729 | 0.9730   | 0.9459  |\n",
+    "| Neural Network (TensorFlow 1HL)   | 0.9250   | 0.9250    | 1.0000 | 0.9610   | 0.9820  |\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Observations\n",
+    "\n",
+    "1. **Best Accuracy & F1-score**:\n",
+    "\n",
+    "   * Logistic Regression (no regularization) and sklearn Neural Network (MLP) achieved the **highest accuracy (0.95)** and **F1-score (~0.973)**.\n",
+    "\n",
+    "2. **Recall vs Precision trade-off**:\n",
+    "\n",
+    "   * TensorFlow Neural Network and tree-based models (Random Forest, XGBoost) have **perfect recall (1.0)**, but slightly lower precision (~0.925).\n",
+    "   * Logistic Regression (L2 weighted) is extremely precise (1.0) but misses some positives (recall 0.8108).\n",
+    "\n",
+    "3. **ROC-AUC**:\n",
+    "\n",
+    "   * TensorFlow Neural Network achieved **highest ROC-AUC (0.982)**, indicating excellent discrimination between classes.\n",
+    "   * Decision Tree has lower ROC-AUC (0.8063), despite good F1-score.\n",
+    "\n",
+    "4. **Sklearn MLP vs TensorFlow**:\n",
+    "\n",
+    "   * Both are similar in architecture, but **different frameworks, initialization, and optimization** lead to slight metric differences.\n",
+    "   * TensorFlow gives slightly better ROC-AUC, but sklearn MLP has slightly higher F1-score.\n",
+    "\n",
+    "5. **Tree-based models**:\n",
+    "\n",
+    "   * Random Forest and XGBoost achieve **perfect recall**, which is important for highly skewed datasets, but precision is slightly lower, leading to a lower F1-score compared to logistic regression.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Conclusion\n",
+    "\n",
+    "* For **balanced performance** (accuracy, precision, recall, F1, ROC-AUC), **Logistic Regression (no regularization) or sklearn Neural Network** are top choices.\n",
+    "* For **maximizing recall** (capturing all price-up movements), **Random Forest, XGBoost, or TensorFlow Neural Network** are better.\n",
+    "* Choice of model depends on business priority:\n",
+    "\n",
+    "  * **Precision-focused** → Logistic Regression (weighted)\n",
+    "  * **Recall-focused** → TensorFlow NN, Random Forest, XGBoost\n",
+    "* Overall, **even simple models perform very well** due to the informative features and engineered technical indicators in the dataset.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bc5d1f4",
+   "metadata": {},
+   "source": [
+    "## 4.4 Analysis & Financial Interpretation\n",
+    "\n",
+    "### 1. Why Logistic Regression Performs Reasonably Good or Bad\n",
+    "\n",
+    "**Observations from results:**\n",
+    "\n",
+    "* Logistic Regression (no regularization) achieved **high accuracy (~0.95)** and **high F1 (~0.973)**.\n",
+    "* Weighted L2 Logistic Regression improves handling of class imbalance but slightly reduces recall.\n",
+    "\n",
+    "**Reasons:**\n",
+    "\n",
+    "* **Good performance**: Logistic regression works well because the target is strongly associated with **binary features** (`high_volatility`, `trend_continuation`) and other technical features, which can be linearly separated.\n",
+    "* **Potential issues**: Without regularization, quasi-complete separation (minority class concentrated in very few cells, e.g., `trend_continuation=1 & future_trend=0`) can cause **unstable coefficient estimates**.\n",
+    "* **Class imbalance handling** is crucial: the dataset is **heavily skewed** (`future_trend=1` is 187 vs 0 is 13). Weighted logistic regression addresses this but can reduce precision slightly.\n",
+    "\n",
+    "**Conclusion:** Logistic Regression performs well for **linear relationships** and strong binary signals but may struggle if feature interactions or non-linearities dominate.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 2. Why Neural Network Performs Better or Worse\n",
+    "\n",
+    "**Observations from results:**\n",
+    "\n",
+    "* TensorFlow NN and sklearn MLP achieve **perfect recall** for some models and high ROC-AUC (~0.982).\n",
+    "* Slightly lower precision (~0.925) for deep models compared to logistic regression.\n",
+    "\n",
+    "**Reasons:**\n",
+    "\n",
+    "* **Better**: Neural networks can capture **non-linear interactions** between numerical features like `technical_score`, `slope_strength`, `candlestick_variance`, etc., which logistic regression cannot.\n",
+    "* **Worse**: In some cases, overfitting can occur, especially with **small minority class counts**, leading to lower precision. Dropout or regularization is necessary to stabilize performance.\n",
+    "* **Random initialization** and training dynamics cause slight differences between sklearn MLP and TensorFlow implementations.\n",
+    "\n",
+    "**Conclusion:** Neural networks are powerful for capturing non-linear patterns but require careful handling of **imbalanced classes** and small sample effects.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 3. Effect of Volatility on Predictions (`high_volatility`)\n",
+    "\n",
+    "| high_volatility | future_trend=0 | future_trend=1 |\n",
+    "| --------------- | -------------- | -------------- |\n",
+    "| 0               | 1              | 127            |\n",
+    "| 1               | 12             | 60             |\n",
+    "\n",
+    "**Interpretation:**\n",
+    "\n",
+    "* Low volatility (`0`) mostly aligns with **future_trend=1**, which makes prediction easier.\n",
+    "* High volatility (`1`) contains **more mix** (12 down vs 60 up), which increases **prediction uncertainty**.\n",
+    "\n",
+    "**Modeling impact:**\n",
+    "\n",
+    "* `high_volatility` is **highly informative** but introduces quasi-separation risk.\n",
+    "* Logistic regression coefficients for this feature may be very large without regularization.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 4. Role of Trend Continuation (`trend_continuation`)\n",
+    "\n",
+    "| trend_continuation | future_trend=0 | future_trend=1 |\n",
+    "| ------------------ | -------------- | -------------- |\n",
+    "| 0                  | 12             | 66             |\n",
+    "| 1                  | 1              | 121            |\n",
+    "\n",
+    "**Interpretation:**\n",
+    "\n",
+    "* Trend continuation strongly correlates with **future_trend=1**.\n",
+    "* Cases with `trend_continuation=0` have a mix, mostly uptrend, but a few downtrend samples increase uncertainty.\n",
+    "\n",
+    "**Modeling impact:**\n",
+    "\n",
+    "* This feature carries **very strong signal** for predicting uptrends.\n",
+    "* Logistic regression without regularization could overfit due to quasi-complete separation.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 5. Situations Where the Model Fails and Why\n",
+    "\n",
+    "**Potential failure cases:**\n",
+    "\n",
+    "1. **High volatility with downtrend (`high_volatility=1` and `future_trend=0`)**\n",
+    "\n",
+    "   * Very few samples (12) → model may **misclassify minority cases**.\n",
+    "2. **No trend continuation but downtrend (`trend_continuation=0` and `future_trend=0`)**\n",
+    "\n",
+    "   * Rare occurrences (12 out of 13 downtrends) → prediction may fail due to small sample size.\n",
+    "3. **Complex non-linear interactions among numerical features**\n",
+    "\n",
+    "   * Logistic regression may fail to capture interactions between `slope_strength`, `candlestick_variance`, `pattern_symmetry`, etc., leading to reduced performance in borderline cases.\n",
+    "\n",
+    "**Why:**\n",
+    "\n",
+    "* Skewed target distribution (`future_trend=1` dominates)\n",
+    "* Quasi-complete separation in binary features\n",
+    "* Small sample size for minority class → instability in coefficients and predictions\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Final Conclusion\n",
+    "\n",
+    "1. **Binary features** (`high_volatility`, `trend_continuation`) dominate predictive power.\n",
+    "2. **Logistic Regression** works well due to linear separability but needs **regularization** and **class weighting**.\n",
+    "3. **Neural Networks** can capture **non-linear interactions**, achieving high recall and ROC-AUC but slightly lower precision.\n",
+    "4. **High volatility** increases uncertainty; models are more likely to misclassify rare downtrends.\n",
+    "5. **Trend continuation** is a very strong predictor of uptrends.\n",
+    "6. **Model failures** mostly occur for **minority class combinations** with very few samples.\n",
+    "\n",
+    "**Recommendation:**\n",
+    "\n",
+    "* Use **class-weighted regularized logistic regression** for interpretability.\n",
+    "* Use **deep neural networks or tree-based models** when non-linear patterns are important.\n",
+    "* Always monitor **rare combinations of binary features** to prevent overconfidence in predictions.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31c8e720",
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+   "source": []
+  }
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