diff --git a/.ipynb_checkpoints/main-checkpoint.ipynb b/.ipynb_checkpoints/main-checkpoint.ipynb
index 10a09d0..774458a 100644
--- a/.ipynb_checkpoints/main-checkpoint.ipynb
+++ b/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 53,
    "id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
    "metadata": {
     "tags": []
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 54,
    "id": "134d0b3d-5481-4975-af07-c80ab09d6dd2",
    "metadata": {
     "tags": []
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 57,
    "id": "d220561a-34c9-48d2-8e2f-5d174a87540b",
    "metadata": {
     "tags": []
@@ -181,10 +181,9 @@
     "                \"Wash_Ratio\", \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
     "        df = pd.DataFrame(row_list, columns = cols)\n",
     "        df[[\"Credit_transaction_amount\",\n",
-    "                \"Debit_transaction_amount\",\n",
-    "                \"Wash_Ratio\"]] = df[[\"Credit_transaction_amount\",\n",
-    "                \"Debit_transaction_amount\",\n",
-    "                \"Wash_Ratio\"]].astype('int')\n",
+    "                \"Debit_transaction_amount\"]] = df[[\"Credit_transaction_amount\",\n",
+    "                \"Debit_transaction_amount\"]].astype('int')\n",
+    "        df[\"Wash_Ratio\"] = df[\"Wash_Ratio\"].astype('float')\n",
     "        # Step 1: Compute 90th percentiles per Segment for all 3 fields\n",
     "        percentiles = (\n",
     "            df.groupby(\"Segment\")[[\"Credit_transaction_amount\",\n",
@@ -223,27 +222,294 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 58,
    "id": "2e5a0ea9-64cd-4a8d-9a5d-e5e7b36a401a",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# sen = Scenario()\n",
-    "# a = sen.logic()"
+    "sen = Scenario()\n",
+    "a = sen.logic()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 59,
    "id": "830c7ec3-9707-46db-9b27-ac4f9d46a03a",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "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>Focal_id</th>\n",
+       "      <th>Credit_transaction_amount</th>\n",
+       "      <th>Total_no_of_credit_transactions</th>\n",
+       "      <th>Debit_transaction_amount</th>\n",
+       "      <th>Total_no_of_debit_transactions</th>\n",
+       "      <th>Wash_Ratio</th>\n",
+       "      <th>Segment</th>\n",
+       "      <th>Risk</th>\n",
+       "      <th>SAR_FLAG</th>\n",
+       "      <th>P90_Credit</th>\n",
+       "      <th>P90_Debit</th>\n",
+       "      <th>P90_Wash</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PN478710</td>\n",
+       "      <td>2805352312</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>2787126309</td>\n",
+       "      <td>2025</td>\n",
+       "      <td>1.006539</td>\n",
+       "      <td>Corporate Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>6.274828e+09</td>\n",
+       "      <td>6.259298e+09</td>\n",
+       "      <td>1.090121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PN483125</td>\n",
+       "      <td>3890052135</td>\n",
+       "      <td>2797</td>\n",
+       "      <td>3968882113</td>\n",
+       "      <td>2850</td>\n",
+       "      <td>0.980138</td>\n",
+       "      <td>Govt. Entities</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>6.112897e+09</td>\n",
+       "      <td>6.072409e+09</td>\n",
+       "      <td>1.112059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PN890403</td>\n",
+       "      <td>4136296083</td>\n",
+       "      <td>2937</td>\n",
+       "      <td>3999785063</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>1.034130</td>\n",
+       "      <td>SME</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>5.709904e+09</td>\n",
+       "      <td>5.559419e+09</td>\n",
+       "      <td>1.118816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PN531475</td>\n",
+       "      <td>4183673982</td>\n",
+       "      <td>2861</td>\n",
+       "      <td>3987068168</td>\n",
+       "      <td>2770</td>\n",
+       "      <td>1.049311</td>\n",
+       "      <td>Corporate Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>6.274828e+09</td>\n",
+       "      <td>6.259298e+09</td>\n",
+       "      <td>1.090121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PN147722</td>\n",
+       "      <td>1775594615</td>\n",
+       "      <td>1225</td>\n",
+       "      <td>1641559222</td>\n",
+       "      <td>1221</td>\n",
+       "      <td>1.081651</td>\n",
+       "      <td>SME</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>5.709904e+09</td>\n",
+       "      <td>5.559419e+09</td>\n",
+       "      <td>1.118816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10009</th>\n",
+       "      <td>PN955059</td>\n",
+       "      <td>31106290</td>\n",
+       "      <td>264</td>\n",
+       "      <td>25266130</td>\n",
+       "      <td>369</td>\n",
+       "      <td>1.231146</td>\n",
+       "      <td>Priority Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.616620e+07</td>\n",
+       "      <td>5.263062e+07</td>\n",
+       "      <td>1.921224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10010</th>\n",
+       "      <td>PN602067</td>\n",
+       "      <td>29780658</td>\n",
+       "      <td>238</td>\n",
+       "      <td>27796448</td>\n",
+       "      <td>405</td>\n",
+       "      <td>1.071384</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>High Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.897534e+07</td>\n",
+       "      <td>5.488447e+07</td>\n",
+       "      <td>1.931817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10011</th>\n",
+       "      <td>PN213487</td>\n",
+       "      <td>41410071</td>\n",
+       "      <td>274</td>\n",
+       "      <td>23896844</td>\n",
+       "      <td>368</td>\n",
+       "      <td>1.732868</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.897534e+07</td>\n",
+       "      <td>5.488447e+07</td>\n",
+       "      <td>1.931817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10012</th>\n",
+       "      <td>PN563065</td>\n",
+       "      <td>34009021</td>\n",
+       "      <td>251</td>\n",
+       "      <td>32563582</td>\n",
+       "      <td>375</td>\n",
+       "      <td>1.044388</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.897534e+07</td>\n",
+       "      <td>5.488447e+07</td>\n",
+       "      <td>1.931817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10013</th>\n",
+       "      <td>PN388875</td>\n",
+       "      <td>30904340</td>\n",
+       "      <td>236</td>\n",
+       "      <td>21938266</td>\n",
+       "      <td>344</td>\n",
+       "      <td>1.408696</td>\n",
+       "      <td>Mass Market</td>\n",
+       "      <td>Medium Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.921967e+07</td>\n",
+       "      <td>5.290545e+07</td>\n",
+       "      <td>1.915159</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10014 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Focal_id  Credit_transaction_amount  Total_no_of_credit_transactions  \\\n",
+       "0      PN478710                 2805352312                             2020   \n",
+       "1      PN483125                 3890052135                             2797   \n",
+       "2      PN890403                 4136296083                             2937   \n",
+       "3      PN531475                 4183673982                             2861   \n",
+       "4      PN147722                 1775594615                             1225   \n",
+       "...         ...                        ...                              ...   \n",
+       "10009  PN955059                   31106290                              264   \n",
+       "10010  PN602067                   29780658                              238   \n",
+       "10011  PN213487                   41410071                              274   \n",
+       "10012  PN563065                   34009021                              251   \n",
+       "10013  PN388875                   30904340                              236   \n",
+       "\n",
+       "       Debit_transaction_amount  Total_no_of_debit_transactions  Wash_Ratio  \\\n",
+       "0                    2787126309                            2025    1.006539   \n",
+       "1                    3968882113                            2850    0.980138   \n",
+       "2                    3999785063                            2824    1.034130   \n",
+       "3                    3987068168                            2770    1.049311   \n",
+       "4                    1641559222                            1221    1.081651   \n",
+       "...                         ...                             ...         ...   \n",
+       "10009                  25266130                             369    1.231146   \n",
+       "10010                  27796448                             405    1.071384   \n",
+       "10011                  23896844                             368    1.732868   \n",
+       "10012                  32563582                             375    1.044388   \n",
+       "10013                  21938266                             344    1.408696   \n",
+       "\n",
+       "                 Segment         Risk SAR_FLAG    P90_Credit     P90_Debit  \\\n",
+       "0      Corporate Banking     Low Risk        N  6.274828e+09  6.259298e+09   \n",
+       "1         Govt. Entities     Low Risk        N  6.112897e+09  6.072409e+09   \n",
+       "2                    SME     Low Risk        N  5.709904e+09  5.559419e+09   \n",
+       "3      Corporate Banking     Low Risk        N  6.274828e+09  6.259298e+09   \n",
+       "4                    SME     Low Risk        N  5.709904e+09  5.559419e+09   \n",
+       "...                  ...          ...      ...           ...           ...   \n",
+       "10009   Priority Banking     Low Risk        N  7.616620e+07  5.263062e+07   \n",
+       "10010             Others    High Risk        N  7.897534e+07  5.488447e+07   \n",
+       "10011             Others     Low Risk        N  7.897534e+07  5.488447e+07   \n",
+       "10012             Others     Low Risk        N  7.897534e+07  5.488447e+07   \n",
+       "10013        Mass Market  Medium Risk        N  7.921967e+07  5.290545e+07   \n",
+       "\n",
+       "       P90_Wash  \n",
+       "0      1.090121  \n",
+       "1      1.112059  \n",
+       "2      1.118816  \n",
+       "3      1.090121  \n",
+       "4      1.118816  \n",
+       "...         ...  \n",
+       "10009  1.921224  \n",
+       "10010  1.931817  \n",
+       "10011  1.931817  \n",
+       "10012  1.931817  \n",
+       "10013  1.915159  \n",
+       "\n",
+       "[10014 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# a"
+    "a"
    ]
   },
   {
diff --git a/main.ipynb b/main.ipynb
index 10a09d0..774458a 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 53,
    "id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
    "metadata": {
     "tags": []
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 54,
    "id": "134d0b3d-5481-4975-af07-c80ab09d6dd2",
    "metadata": {
     "tags": []
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 57,
    "id": "d220561a-34c9-48d2-8e2f-5d174a87540b",
    "metadata": {
     "tags": []
@@ -181,10 +181,9 @@
     "                \"Wash_Ratio\", \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
     "        df = pd.DataFrame(row_list, columns = cols)\n",
     "        df[[\"Credit_transaction_amount\",\n",
-    "                \"Debit_transaction_amount\",\n",
-    "                \"Wash_Ratio\"]] = df[[\"Credit_transaction_amount\",\n",
-    "                \"Debit_transaction_amount\",\n",
-    "                \"Wash_Ratio\"]].astype('int')\n",
+    "                \"Debit_transaction_amount\"]] = df[[\"Credit_transaction_amount\",\n",
+    "                \"Debit_transaction_amount\"]].astype('int')\n",
+    "        df[\"Wash_Ratio\"] = df[\"Wash_Ratio\"].astype('float')\n",
     "        # Step 1: Compute 90th percentiles per Segment for all 3 fields\n",
     "        percentiles = (\n",
     "            df.groupby(\"Segment\")[[\"Credit_transaction_amount\",\n",
@@ -223,27 +222,294 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 58,
    "id": "2e5a0ea9-64cd-4a8d-9a5d-e5e7b36a401a",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# sen = Scenario()\n",
-    "# a = sen.logic()"
+    "sen = Scenario()\n",
+    "a = sen.logic()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 59,
    "id": "830c7ec3-9707-46db-9b27-ac4f9d46a03a",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "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>Focal_id</th>\n",
+       "      <th>Credit_transaction_amount</th>\n",
+       "      <th>Total_no_of_credit_transactions</th>\n",
+       "      <th>Debit_transaction_amount</th>\n",
+       "      <th>Total_no_of_debit_transactions</th>\n",
+       "      <th>Wash_Ratio</th>\n",
+       "      <th>Segment</th>\n",
+       "      <th>Risk</th>\n",
+       "      <th>SAR_FLAG</th>\n",
+       "      <th>P90_Credit</th>\n",
+       "      <th>P90_Debit</th>\n",
+       "      <th>P90_Wash</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PN478710</td>\n",
+       "      <td>2805352312</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>2787126309</td>\n",
+       "      <td>2025</td>\n",
+       "      <td>1.006539</td>\n",
+       "      <td>Corporate Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>6.274828e+09</td>\n",
+       "      <td>6.259298e+09</td>\n",
+       "      <td>1.090121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PN483125</td>\n",
+       "      <td>3890052135</td>\n",
+       "      <td>2797</td>\n",
+       "      <td>3968882113</td>\n",
+       "      <td>2850</td>\n",
+       "      <td>0.980138</td>\n",
+       "      <td>Govt. Entities</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>6.112897e+09</td>\n",
+       "      <td>6.072409e+09</td>\n",
+       "      <td>1.112059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PN890403</td>\n",
+       "      <td>4136296083</td>\n",
+       "      <td>2937</td>\n",
+       "      <td>3999785063</td>\n",
+       "      <td>2824</td>\n",
+       "      <td>1.034130</td>\n",
+       "      <td>SME</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>5.709904e+09</td>\n",
+       "      <td>5.559419e+09</td>\n",
+       "      <td>1.118816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PN531475</td>\n",
+       "      <td>4183673982</td>\n",
+       "      <td>2861</td>\n",
+       "      <td>3987068168</td>\n",
+       "      <td>2770</td>\n",
+       "      <td>1.049311</td>\n",
+       "      <td>Corporate Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>6.274828e+09</td>\n",
+       "      <td>6.259298e+09</td>\n",
+       "      <td>1.090121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PN147722</td>\n",
+       "      <td>1775594615</td>\n",
+       "      <td>1225</td>\n",
+       "      <td>1641559222</td>\n",
+       "      <td>1221</td>\n",
+       "      <td>1.081651</td>\n",
+       "      <td>SME</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>5.709904e+09</td>\n",
+       "      <td>5.559419e+09</td>\n",
+       "      <td>1.118816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10009</th>\n",
+       "      <td>PN955059</td>\n",
+       "      <td>31106290</td>\n",
+       "      <td>264</td>\n",
+       "      <td>25266130</td>\n",
+       "      <td>369</td>\n",
+       "      <td>1.231146</td>\n",
+       "      <td>Priority Banking</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.616620e+07</td>\n",
+       "      <td>5.263062e+07</td>\n",
+       "      <td>1.921224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10010</th>\n",
+       "      <td>PN602067</td>\n",
+       "      <td>29780658</td>\n",
+       "      <td>238</td>\n",
+       "      <td>27796448</td>\n",
+       "      <td>405</td>\n",
+       "      <td>1.071384</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>High Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.897534e+07</td>\n",
+       "      <td>5.488447e+07</td>\n",
+       "      <td>1.931817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10011</th>\n",
+       "      <td>PN213487</td>\n",
+       "      <td>41410071</td>\n",
+       "      <td>274</td>\n",
+       "      <td>23896844</td>\n",
+       "      <td>368</td>\n",
+       "      <td>1.732868</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.897534e+07</td>\n",
+       "      <td>5.488447e+07</td>\n",
+       "      <td>1.931817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10012</th>\n",
+       "      <td>PN563065</td>\n",
+       "      <td>34009021</td>\n",
+       "      <td>251</td>\n",
+       "      <td>32563582</td>\n",
+       "      <td>375</td>\n",
+       "      <td>1.044388</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>Low Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.897534e+07</td>\n",
+       "      <td>5.488447e+07</td>\n",
+       "      <td>1.931817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10013</th>\n",
+       "      <td>PN388875</td>\n",
+       "      <td>30904340</td>\n",
+       "      <td>236</td>\n",
+       "      <td>21938266</td>\n",
+       "      <td>344</td>\n",
+       "      <td>1.408696</td>\n",
+       "      <td>Mass Market</td>\n",
+       "      <td>Medium Risk</td>\n",
+       "      <td>N</td>\n",
+       "      <td>7.921967e+07</td>\n",
+       "      <td>5.290545e+07</td>\n",
+       "      <td>1.915159</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10014 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Focal_id  Credit_transaction_amount  Total_no_of_credit_transactions  \\\n",
+       "0      PN478710                 2805352312                             2020   \n",
+       "1      PN483125                 3890052135                             2797   \n",
+       "2      PN890403                 4136296083                             2937   \n",
+       "3      PN531475                 4183673982                             2861   \n",
+       "4      PN147722                 1775594615                             1225   \n",
+       "...         ...                        ...                              ...   \n",
+       "10009  PN955059                   31106290                              264   \n",
+       "10010  PN602067                   29780658                              238   \n",
+       "10011  PN213487                   41410071                              274   \n",
+       "10012  PN563065                   34009021                              251   \n",
+       "10013  PN388875                   30904340                              236   \n",
+       "\n",
+       "       Debit_transaction_amount  Total_no_of_debit_transactions  Wash_Ratio  \\\n",
+       "0                    2787126309                            2025    1.006539   \n",
+       "1                    3968882113                            2850    0.980138   \n",
+       "2                    3999785063                            2824    1.034130   \n",
+       "3                    3987068168                            2770    1.049311   \n",
+       "4                    1641559222                            1221    1.081651   \n",
+       "...                         ...                             ...         ...   \n",
+       "10009                  25266130                             369    1.231146   \n",
+       "10010                  27796448                             405    1.071384   \n",
+       "10011                  23896844                             368    1.732868   \n",
+       "10012                  32563582                             375    1.044388   \n",
+       "10013                  21938266                             344    1.408696   \n",
+       "\n",
+       "                 Segment         Risk SAR_FLAG    P90_Credit     P90_Debit  \\\n",
+       "0      Corporate Banking     Low Risk        N  6.274828e+09  6.259298e+09   \n",
+       "1         Govt. Entities     Low Risk        N  6.112897e+09  6.072409e+09   \n",
+       "2                    SME     Low Risk        N  5.709904e+09  5.559419e+09   \n",
+       "3      Corporate Banking     Low Risk        N  6.274828e+09  6.259298e+09   \n",
+       "4                    SME     Low Risk        N  5.709904e+09  5.559419e+09   \n",
+       "...                  ...          ...      ...           ...           ...   \n",
+       "10009   Priority Banking     Low Risk        N  7.616620e+07  5.263062e+07   \n",
+       "10010             Others    High Risk        N  7.897534e+07  5.488447e+07   \n",
+       "10011             Others     Low Risk        N  7.897534e+07  5.488447e+07   \n",
+       "10012             Others     Low Risk        N  7.897534e+07  5.488447e+07   \n",
+       "10013        Mass Market  Medium Risk        N  7.921967e+07  5.290545e+07   \n",
+       "\n",
+       "       P90_Wash  \n",
+       "0      1.090121  \n",
+       "1      1.112059  \n",
+       "2      1.118816  \n",
+       "3      1.090121  \n",
+       "4      1.118816  \n",
+       "...         ...  \n",
+       "10009  1.921224  \n",
+       "10010  1.931817  \n",
+       "10011  1.931817  \n",
+       "10012  1.931817  \n",
+       "10013  1.915159  \n",
+       "\n",
+       "[10014 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# a"
+    "a"
    ]
   },
   {
diff --git a/main.py b/main.py
index 3e7f4ba..5101e30 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# In[42]:
+# In[53]:
 
 
 import pandas as pd
@@ -20,7 +20,7 @@ seq = SQLQueryInterface(schema="transactionschema")
 seq.execute_raw("show tables")
 
 
-# In[44]:
+# In[54]:
 
 
 query = """
@@ -114,7 +114,7 @@ query = """
 """
 
 
-# In[45]:
+# In[57]:
 
 
 from tms_data_interface import SQLQueryInterface
@@ -134,10 +134,9 @@ class Scenario:
                 "Wash_Ratio", "Segment", "Risk", "SAR_FLAG"]
         df = pd.DataFrame(row_list, columns = cols)
         df[["Credit_transaction_amount",
-                "Debit_transaction_amount",
-                "Wash_Ratio"]] = df[["Credit_transaction_amount",
-                "Debit_transaction_amount",
-                "Wash_Ratio"]].astype('int')
+                "Debit_transaction_amount"]] = df[["Credit_transaction_amount",
+                "Debit_transaction_amount"]].astype('int')
+        df["Wash_Ratio"] = df["Wash_Ratio"].astype('float')
         # Step 1: Compute 90th percentiles per Segment for all 3 fields
         percentiles = (
             df.groupby("Segment")[["Credit_transaction_amount",
@@ -174,17 +173,17 @@ class Scenario:
         return df
 
 
-# In[51]:
+# In[58]:
 
 
-# sen = Scenario()
-# a = sen.logic()
+sen = Scenario()
+a = sen.logic()
 
 
-# In[50]:
+# In[59]:
 
 
-# a
+a
 
 
 # In[49]: