diff --git a/notebooks/02_rq_eda.ipynb b/notebooks/02_rq_eda.ipynb
index 988df87..4eba372 100644
--- a/notebooks/02_rq_eda.ipynb
+++ b/notebooks/02_rq_eda.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 76,
    "id": "bbaa676b",
    "metadata": {},
    "outputs": [],
@@ -11,12 +11,12 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n"
+    "import seaborn as sns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "f484ce3a",
    "metadata": {},
    "outputs": [],
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "8e245485",
    "metadata": {},
    "outputs": [
@@ -229,7 +229,7 @@
        "4   3.99  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "2aa61a7e",
    "metadata": {},
    "outputs": [],
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "a245e29e",
    "metadata": {},
    "outputs": [],
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "e343f566",
    "metadata": {},
    "outputs": [],
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "b1adcdf8",
    "metadata": {},
    "outputs": [
@@ -325,7 +325,7 @@
        "Name: success, Length: 27075, dtype: float64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "225e849a",
    "metadata": {},
    "outputs": [
@@ -422,7 +422,7 @@
        "4              5250               288           5538      0.947996    5250.0"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -433,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "185dc896",
    "metadata": {},
    "outputs": [
@@ -815,7 +815,7 @@
        "[10 rows x 21 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -828,7 +828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "0f631e8c",
    "metadata": {},
    "outputs": [
@@ -855,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "e7c93631",
    "metadata": {},
    "outputs": [
@@ -873,7 +873,7 @@
        "Name: success, dtype: float64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -884,7 +884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "bbe3c9ca",
    "metadata": {},
    "outputs": [
@@ -894,7 +894,7 @@
        "Text(0.5, 1.0, 'Success Distribution')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -916,7 +916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "a4c6b8f7",
    "metadata": {},
    "outputs": [],
@@ -927,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "50f728f8",
    "metadata": {},
    "outputs": [
@@ -937,7 +937,7 @@
        "Text(0.5, 1.0, 'Log Success Distribution')"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -975,7 +975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "1c15bd6b",
    "metadata": {},
    "outputs": [
@@ -1023,7 +1023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "db4a5a01",
    "metadata": {},
    "outputs": [
@@ -1066,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "id": "d285cf1d",
    "metadata": {},
    "outputs": [
@@ -1114,6 +1114,66 @@
     "------------------------------------------------------------------------------------------------------------------------"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "08e39ea4",
+   "metadata": {},
+   "source": [
+    "## Correlation Heatmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "9d687e23",
+   "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",
+    "\n",
+    "sns.heatmap(\n",
+    "    steam[[\n",
+    "        \"success\", \n",
+    "        \"total_reviews\", \n",
+    "        \"average_playtime\", \n",
+    "        \"review_score\", \n",
+    "        \"price\"\n",
+    "    ]].corr(),\n",
+    "    annot=True,\n",
+    "    fmt=\".2f\",\n",
+    "    cmap=\"coolwarm\"\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Correlation Matrix\", fontsize=14)\n",
+    "\n",
+    "# Rotate axis labels for readability\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.yticks(rotation=0)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f7018b4",
+   "metadata": {},
+   "source": [
+    "* Success vs Reviews 0.9 correlation is mathematically expected since success formula consists in total_reviews * review_score.\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4d3e4247",
@@ -1141,7 +1201,7 @@
     "\n",
     "    * Do free-to-play games achieve higher engagement?\n",
     "\n",
-    "    * Are premium-priced games associated with higher or lower review scores? "
+    "    * Are higher-priced games associated with higher or lower review scores? "
    ]
   },
   {
@@ -1172,26 +1232,1550 @@
     "* H2: Games with more reviews tend to achieve higher success.\n",
     "* H3: Games with higher average playtime tend to be more successful.\n",
     "* H4: Game success on Steam is highly concentrated among small number of titles.\n",
-    "* H5: There is an optimal price range that maximizes game success."
+    "* H5: There is an optimal price range that maximizes game success.\n",
+    "\n",
+    "------------------------------------------------------------------------------------------------\n",
+    "------------------------------------------------------------------------------------------------\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d95a97c7",
+   "metadata": {},
+   "source": [
+    "### H1: Price vs Success\n",
+    "(Lower-priced games achieve higher success than high-priced games)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e831bad8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price_category\n",
+       "Upper Mid     5513.165733\n",
+       "Free          3065.403125\n",
+       "Lower Mid     1853.017754\n",
+       "Cheap          792.838413\n",
+       "Expensive      159.633333\n",
+       "Very Cheap     143.650342\n",
+       "Name: success, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steam[\"price_category\"] = \"Paid\"\n",
+    "\n",
+    "steam.loc[steam[\"price\"]== 0, \"price_category\"] = \"Free\"\n",
+    "\n",
+    "price_bins = [0, 5, 10, 20, 60, 1000]\n",
+    "price_labels = [\"Very Cheap\", \"Cheap\", \"Lower Mid\", \"Upper Mid\", \"Expensive\"]\n",
+    "# SABINA COMMENT: Would make a Very Expensive category too, to keep the same naming conventions everywhere!\n",
+    "\n",
+    "steam.loc[steam[\"price\"] > 0, \"price_category\"] = pd.cut(\n",
+    "    steam.loc[steam[\"price\"] > 0, \"price\"],\n",
+    "    bins=price_bins,\n",
+    "    labels=price_labels\n",
+    ")\n",
+    "\n",
+    "price_analysis = steam.groupby(\"price_category\")[\"success\"].mean().sort_values(ascending= False)\n",
+    "\n",
+    "price_analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "39af54ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price_category\n",
+       "Upper Mid     399.0\n",
+       "Lower Mid      88.0\n",
+       "Free           58.5\n",
+       "Cheap          26.0\n",
+       "Expensive      23.5\n",
+       "Very Cheap     14.0\n",
+       "Name: success, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steam.groupby(\"price_category\")[\"success\"].median().sort_values(ascending=False)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e92d454d",
+   "id": "e708bb33",
    "metadata": {},
    "source": [
-    "This hypothesis testing will be done in SQL, to do this I need to create a cleaned dataframe that contains all the columns needed \n",
-    "(steam.csv original columns + columns created for EDA)."
+    "SABINA COMMENT: I think I would've gone for a scatterplot + correlation coefficient for this as both variables are continuous. I assume you were thinking about making the analysis easier to understand to someone else?\n",
+    "\n",
+    "I think you can do the binning after you check the correlation with the appropriate mathematical method as it's always going to be more accurate :)\n",
+    "\n",
+    "Also, that's a very big jump in Upper Mid... isn't this just an outlier? Would add a boxplot here to look at the success ranges across the different price bins."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "566dd8b8",
+   "id": "db6d5453",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price_category\n",
+       "Very Cheap    13293\n",
+       "Cheap          6300\n",
+       "Lower Mid      3999\n",
+       "Free           2560\n",
+       "Upper Mid       893\n",
+       "Expensive        30\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steam[\"price_category\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00c622b0",
+   "metadata": {},
+   "source": [
+    "The analysis suggests that success on Steam is highest for games in the upper-mid price range, followed by free-to-play titles. \n",
+    "\n",
+    "SABINA COMMENT: Isn't it upper-mid, then lower-mid, then free-to-play?\n",
+    "\n",
+    "Very cheap and very expensive games show the lowest average success. This indicates that there may be an optimal pricing range rather than a simple pattern where lower prices always lead to better performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "771c6781",
+   "metadata": {},
+   "source": [
+    "Advice for companies/publishers:\n",
+    "\n",
+    "* Avoid assuming that the lowest possible price will maximize success. Games priced in the upper-mid range appear to balance accessibility and perceived value more effectively, while free-to-play can also be a strong strategy when combined with high engagement.\n",
+    "\n",
+    "SABINA COMMENT: Can you also see some player attributes per price range?\n",
+    "\n",
+    "------------------------------------------------------------------------------------------------------------\n",
+    "------------------------------------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7760fd1",
+   "metadata": {},
+   "source": [
+    "### H2: Review vs Success\n",
+    "\n",
+    "(Games with higher average reviews tend to be more successful)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4cbbc8b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\rodri\\AppData\\Local\\Temp\\ipykernel_11604\\2914874560.py:7: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  review_analysis= steam.groupby(\"review_bucket\")[\"success\"].mean()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "review_bucket\n",
+       "0-50         11.193771\n",
+       "50-200       73.988084\n",
+       "200+        336.478836\n",
+       "1k+        2472.255288\n",
+       "10k+      21033.228571\n",
+       "Name: success, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steam['review_bucket'] = pd.cut(\n",
+    "    steam[\"total_reviews\"],\n",
+    "    bins=[0, 50, 200, 1000, 10000, 100000],\n",
+    "    labels=[\"0-50\", \"50-200\", \"200+\", \"1k+\", \"10k+\"]\n",
+    ")\n",
+    "\n",
+    "review_analysis= steam.groupby(\"review_bucket\")[\"success\"].mean()\n",
+    "\n",
+    "review_analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a292ae07",
+   "metadata": {},
+   "source": [
+    "Since the \"success\" metric is calculated as review_score * total reviews,\n",
+    "this KPI behaves roughly as an estimated positive review impact, this means:\n",
+    "\n",
+    "SABINA COMMENT: If success is calculated from review_score * total_reviews, you already have the answer to your hypothesis :D \n",
+    "\n",
+    "\n",
+    "The review bucket of 0 to 50 has an average game success of 11.19 (Due to low review count on games with 0 to 50 reviews)\n",
+    "But the review bucket of 10k+ reviews has an average game success of 21033, this is because games with huge review counts are translated to \"more success\" which is mathematically predicted.\n",
+    "\n",
+    "    Example:\n",
+    "\n",
+    "    * If game A has 10,000 reviews:\n",
+    "\n",
+    "    But only 20% reviews are positive -> The review_score is 0.2.\n",
+    "\n",
+    "    So our success metric (review_score * total_reviews) will be: success = 0.2 * 10 000 which equals to 2 000.\n",
+    "\n",
+    "    Comparing this 10k+ bin to a game that only has 50 reviews but they're all positive, how can we affirm that the 10k+ reviewed game has higher success than the 50 reviewed game?\n",
+    "\n",
+    "    * Both games can be classified as unsuccessful since one lacks popularity and the other lacks positive ratings.\n",
+    "\n",
+    "For this reason, review count alone is insufficient to determine true game success. Additional metrics such as playtime are required to better capture player engagement and retention.\n",
+    "\n",
+    "SABINA COMMENT: I really like that you wrote this out and I think it's a good exercise to understand what you're dealing with. However, as soon as we know *for sure* that our variable is equal to a combination of variables, there's no point in making a hypothesis. A hypothesis is something you are trying to prove/disprove when you don't already have the answer.\n",
+    "\n",
+    "------------------------------------------------------------------------------------------------------------\n",
+    "------------------------------------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b86e2d7",
+   "metadata": {},
+   "source": [
+    "### H3: Playtime vs Success \n",
+    "(Games with higher average playtime tend to be more successful)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b8430f46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\rodri\\AppData\\Local\\Temp\\ipykernel_11604\\2336436708.py:7: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  playtime_analysis = steam.groupby(\"playtime_bucket\")[\"success\"].mean().sort_values(ascending=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "playtime_bucket\n",
+       "83+hrs      77294.811321\n",
+       "16-83hrs    15688.212810\n",
+       "5-16hrs      3937.003610\n",
+       "1-5hrs       1347.782686\n",
+       "1-60min       546.889377\n",
+       "0              61.721980\n",
+       "Name: success, dtype: float64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steam[\"playtime_bucket\"] = pd.cut(\n",
+    "    steam[\"average_playtime\"],\n",
+    "    bins= [-1, 0, 60, 300, 1000, 5000, steam[\"average_playtime\"].max()],\n",
+    "    labels= [\"0\", \"1-60min\", \"1-5hrs\", \"5-16hrs\", \"16-83hrs\", \"83+hrs\"]\n",
+    ")\n",
+    "\n",
+    "playtime_analysis = steam.groupby(\"playtime_bucket\")[\"success\"].mean().sort_values(ascending=False)\n",
+    "\n",
+    "playtime_analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a91d4b7",
+   "metadata": {},
+   "source": [
+    "As predicted, the more the game is played, the more and positive reviews it gets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "280f605c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "playtime_bucket\n",
+       "0           20905\n",
+       "1-60min      1365\n",
+       "1-5hrs       2830\n",
+       "5-16hrs      1385\n",
+       "16-83hrs      484\n",
+       "83+hrs        106\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check counts per bucket\n",
+    "\n",
+    "steam[\"playtime_bucket\"].value_counts().sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07e111ac",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "steam.to_csv(\"../data/processed/steam_cleaned.csv\", index= False)"
+    "20,905 out of 27,075 games = ~77%, have 0 hours of playtime\n",
+    "\n",
+    "This means the majority of Steam games fail to achieve meaningful player engagement, so Steam is a highly skewed market.\n",
+    "\n",
+    "SABINA COMMENT: That's really fascinating. How come? Can you check also other characteristics like price, game type, game owner, etc.? Also, can anyone just submit a game or what's the process?\n",
+    "\n",
+    "Out of 27k games, only ~600 games reach a high playtime, higher playtime buckets contain significantly fewer games, indicating that strong engagement is rare but potentially associated with higher success."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3d94293f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\rodri\\AppData\\Local\\Temp\\ipykernel_11604\\2315270432.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  steam.groupby(\"playtime_bucket\")[\"review_score\"].mean().sort_values(ascending=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "playtime_bucket\n",
+       "16-83hrs    0.799012\n",
+       "83+hrs      0.777106\n",
+       "5-16hrs     0.770286\n",
+       "1-5hrs      0.721621\n",
+       "1-60min     0.715650\n",
+       "0           0.707462\n",
+       "Name: review_score, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check average review score with playtime\n",
+    "\n",
+    "steam.groupby(\"playtime_bucket\")[\"review_score\"].mean().sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed3b9442",
+   "metadata": {},
+   "source": [
+    "High-playtime games are not only more successful, but also tend to receive better player ratings, the increase is not massive but its significant (+9%).\n",
+    "\n",
+    "SABINA COMMENT: Makes sense, right? If you don't like a game, you're not going to play it for long."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "35dba839",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "playtime_analysis.plot(kind=\"bar\", title=\"Average Success by Playtime Bucket\")\n",
+    "plt.ylabel(\"Average Success\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ae79587",
+   "metadata": {},
+   "source": [
+    "The analysis shows a strong positive relationship between playtime and success. Games with higher average playtime achieve significantly higher success scores, indicating that player engagement and retention are key drivers of performance on Steam. However, high-playtime games represent a relatively small portion of the dataset, suggesting that strong engagement is rare but highly impactful. This highlights the importance of designing games that encourage sustained player interaction.\n",
+    "\n",
+    "SABINA COMMENT: \"designing games that encourage sustained player interaction.\" Does it? I can't help but wonder whether those games are done by big businesses with many millions to spend on marketing like Riot Games. Any ownership data on these games?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2ac13f9",
+   "metadata": {},
+   "source": [
+    "The results from the third hypothesis suggest that game success on Steam may be driven by a relatively small number of highly engaging titles. However, this conclusion is based on the relationship between playtime and success. To confirm whether success is truly concentrated among a limited number of games, a more direct analysis is required, which is addressed in the fourth hypothesis.\n",
+    "\n",
+    "----------------------------------------------------------------------------------------\n",
+    "----------------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3caef70",
+   "metadata": {},
+   "source": [
+    "### H4: Success Concentration\n",
+    "\n",
+    "(Game success on Steam is highly concentrated among small number of titles.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "4971aa8e",
+   "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>name</th>\n",
+       "      <th>success</th>\n",
+       "      <th>cumulative_percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Counter-Strike: Global Offensive</td>\n",
+       "      <td>2644404.0</td>\n",
+       "      <td>0.097615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Dota 2</td>\n",
+       "      <td>863507.0</td>\n",
+       "      <td>0.129490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Team Fortress 2</td>\n",
+       "      <td>515879.0</td>\n",
+       "      <td>0.148533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12836</th>\n",
+       "      <td>PLAYERUNKNOWN'S BATTLEGROUNDS</td>\n",
+       "      <td>496184.0</td>\n",
+       "      <td>0.166850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>Garry's Mod</td>\n",
+       "      <td>363721.0</td>\n",
+       "      <td>0.180276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2478</th>\n",
+       "      <td>Grand Theft Auto V</td>\n",
+       "      <td>329061.0</td>\n",
+       "      <td>0.192423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1467</th>\n",
+       "      <td>PAYDAY 2</td>\n",
+       "      <td>308657.0</td>\n",
+       "      <td>0.203816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3362</th>\n",
+       "      <td>Unturned</td>\n",
+       "      <td>292574.0</td>\n",
+       "      <td>0.214616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1120</th>\n",
+       "      <td>Terraria</td>\n",
+       "      <td>255600.0</td>\n",
+       "      <td>0.224052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Left 4 Dead 2</td>\n",
+       "      <td>251789.0</td>\n",
+       "      <td>0.233346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5235</th>\n",
+       "      <td>Tom Clancy's Rainbow Six® Siege</td>\n",
+       "      <td>251178.0</td>\n",
+       "      <td>0.242618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2031</th>\n",
+       "      <td>Rocket League®</td>\n",
+       "      <td>242561.0</td>\n",
+       "      <td>0.251572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1025</th>\n",
+       "      <td>The Elder Scrolls V: Skyrim</td>\n",
+       "      <td>237303.0</td>\n",
+       "      <td>0.260332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1634</th>\n",
+       "      <td>Warframe</td>\n",
+       "      <td>226541.0</td>\n",
+       "      <td>0.268694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016</th>\n",
+       "      <td>Rust</td>\n",
+       "      <td>220370.0</td>\n",
+       "      <td>0.276829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2964</th>\n",
+       "      <td>The Witcher® 3: Wild Hunt</td>\n",
+       "      <td>202930.0</td>\n",
+       "      <td>0.284320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1596</th>\n",
+       "      <td>Euro Truck Simulator 2</td>\n",
+       "      <td>176769.0</td>\n",
+       "      <td>0.290845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8129</th>\n",
+       "      <td>Paladins®</td>\n",
+       "      <td>169580.0</td>\n",
+       "      <td>0.297105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4712</th>\n",
+       "      <td>ARK: Survival Evolved</td>\n",
+       "      <td>145035.0</td>\n",
+       "      <td>0.302459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>903</th>\n",
+       "      <td>Borderlands 2</td>\n",
+       "      <td>144595.0</td>\n",
+       "      <td>0.307796</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   name    success  cumulative_percentage\n",
+       "25     Counter-Strike: Global Offensive  2644404.0               0.097615\n",
+       "22                               Dota 2   863507.0               0.129490\n",
+       "19                      Team Fortress 2   515879.0               0.148533\n",
+       "12836     PLAYERUNKNOWN'S BATTLEGROUNDS   496184.0               0.166850\n",
+       "121                         Garry's Mod   363721.0               0.180276\n",
+       "2478                 Grand Theft Auto V   329061.0               0.192423\n",
+       "1467                           PAYDAY 2   308657.0               0.203816\n",
+       "3362                           Unturned   292574.0               0.214616\n",
+       "1120                           Terraria   255600.0               0.224052\n",
+       "21                        Left 4 Dead 2   251789.0               0.233346\n",
+       "5235    Tom Clancy's Rainbow Six® Siege   251178.0               0.242618\n",
+       "2031                     Rocket League®   242561.0               0.251572\n",
+       "1025        The Elder Scrolls V: Skyrim   237303.0               0.260332\n",
+       "1634                           Warframe   226541.0               0.268694\n",
+       "2016                               Rust   220370.0               0.276829\n",
+       "2964          The Witcher® 3: Wild Hunt   202930.0               0.284320\n",
+       "1596             Euro Truck Simulator 2   176769.0               0.290845\n",
+       "8129                          Paladins®   169580.0               0.297105\n",
+       "4712              ARK: Survival Evolved   145035.0               0.302459\n",
+       "903                       Borderlands 2   144595.0               0.307796"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steam_sorted = steam.sort_values(\"success\", ascending = False)\n",
+    "\n",
+    "steam_sorted[\"cumulative_success\"] = steam_sorted[\"success\"].cumsum()\n",
+    "\n",
+    "total_success = steam[\"success\"].sum()\n",
+    "\n",
+    "steam_sorted[\"cumulative_percentage\"] = steam_sorted[\"cumulative_success\"] / total_success\n",
+    "\n",
+    "steam_sorted[[\"name\", \"success\", \"cumulative_percentage\"]].head(20)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5de40c2",
+   "metadata": {},
+   "source": [
+    "This Top 20 games list are ~0.07% of all Steam games (20 games out of 27k) yet they generate 30.7% of total success.\n",
+    "\n",
+    "SABINA COMMENT: I like this, well done! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b4e5a093",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(736, 0.027183748845798706)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top_games = steam_sorted[steam_sorted[\"cumulative_percentage\"] <= 0.8]\n",
+    "len(top_games), len(top_games) / len(steam)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b574d16d",
+   "metadata": {},
+   "source": [
+    "The analysis shows that game success on Steam is highly concentrated among a very small number of titles. Approximately 2.7% of games account for 80% of total success, indicating a strong winner-takes-most dynamic. Additionally, the top 20 games alone generate over 30% of total success, despite representing less than 0.1% of all titles. This highlights the extreme inequality in game performance on the platform, where only a small fraction of games achieve significant visibility and impact.\n",
+    "\n",
+    "This concentration suggests that achieving high success on Steam requires reaching the top-performing segment, as the majority of games receive minimal attention and engagement.\n",
+    "\n",
+    "SABINA COMMENT: Very cool and sad. Divya found similar things when looking at football teams. Same questions about ownership of the games as before!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3915b2d",
+   "metadata": {},
+   "source": [
+    "To complete our business recommendations we analyze the optimal price range with Hypothesis 5\n",
+    "\n",
+    "--------------------------------------------------------------------------------------------------------\n",
+    "--------------------------------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35a7fe03",
+   "metadata": {},
+   "source": [
+    "### H5: Optimal Price range in Steam games\n",
+    "\n",
+    "(There is an optimal price range that maximizes game success)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "0b8f37c3",
+   "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>success</th>\n",
+       "      <th>total_reviews</th>\n",
+       "      <th>review_score</th>\n",
+       "      <th>average_playtime</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>price_category</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Cheap</th>\n",
+       "      <td>792.838413</td>\n",
+       "      <td>894.742222</td>\n",
+       "      <td>0.745003</td>\n",
+       "      <td>88.038730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Expensive</th>\n",
+       "      <td>159.633333</td>\n",
+       "      <td>185.933333</td>\n",
+       "      <td>0.754777</td>\n",
+       "      <td>74.033333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Free</th>\n",
+       "      <td>3065.403125</td>\n",
+       "      <td>3695.566797</td>\n",
+       "      <td>0.719060</td>\n",
+       "      <td>463.128516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lower Mid</th>\n",
+       "      <td>1853.017754</td>\n",
+       "      <td>2161.719430</td>\n",
+       "      <td>0.757227</td>\n",
+       "      <td>193.546387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Upper Mid</th>\n",
+       "      <td>5513.165733</td>\n",
+       "      <td>7487.312430</td>\n",
+       "      <td>0.741594</td>\n",
+       "      <td>851.816349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Very Cheap</th>\n",
+       "      <td>143.650342</td>\n",
+       "      <td>178.266080</td>\n",
+       "      <td>0.684356</td>\n",
+       "      <td>58.589558</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    success  total_reviews  review_score  average_playtime\n",
+       "price_category                                                            \n",
+       "Cheap            792.838413     894.742222      0.745003         88.038730\n",
+       "Expensive        159.633333     185.933333      0.754777         74.033333\n",
+       "Free            3065.403125    3695.566797      0.719060        463.128516\n",
+       "Lower Mid       1853.017754    2161.719430      0.757227        193.546387\n",
+       "Upper Mid       5513.165733    7487.312430      0.741594        851.816349\n",
+       "Very Cheap       143.650342     178.266080      0.684356         58.589558"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For each price category, what is the average performance across all key metrics?\n",
+    "\n",
+    "steam.groupby(\"price_category\")[[\n",
+    "    \"success\", \n",
+    "    \"total_reviews\", \n",
+    "    \"review_score\", \n",
+    "    \"average_playtime\"\n",
+    "]].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf4b7877",
+   "metadata": {},
+   "source": [
+    "This Hypothesis shows us the full performance profile on each price category."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6959038a",
+   "metadata": {},
+   "source": [
+    "The analysis indicates that games priced in the upper-mid range achieve the highest overall performance across all key metrics, including success, popularity, and player engagement. While free-to-play games also demonstrate strong engagement and reach, they tend to have slightly lower review scores. In contrast, very cheap and expensive games show significantly lower performance, suggesting that both underpricing and overpricing can negatively impact success. These findings support the existence of an optimal price range that balances accessibility, perceived value, and player engagement.\n",
+    "\n",
+    "Developers should aim to position their games within the upper-mid price range, as it appears to offer the best balance between attracting players, maintaining engagement, and achieving strong overall performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9acdb1d9",
+   "metadata": {},
+   "source": [
+    "----------------------------------------------------------------------------------------------------------------------------------\n",
+    "----------------------------------------------------------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb71b7c1",
+   "metadata": {},
+   "source": [
+    "## Market and Business Insights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bacbd12",
+   "metadata": {},
+   "source": [
+    "After analyzing game performance from price, success and playtime we now look at Steam games as a market and business, we want to answer questions like:\n",
+    "\n",
+    "* What kind of games win?\n",
+    "* Who makes them?\n",
+    "* Where should Steam invest and why?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eea187f9",
+   "metadata": {},
+   "source": [
+    "For this we come up with two new hypothesis:\n",
+    "\n",
+    "* H6: Which genre of games perform best?\n",
+    "* H7: Who creates successful games?\n",
+    "---------------------------------------------------------------------------------------------\n",
+    "---------------------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "080473fb",
+   "metadata": {},
+   "source": [
+    "### H6: Genre Analysis\n",
+    "\n",
+    "(Which genre of games perform best)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "890f1dd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Since genre column has multiple genres in a row, first we split the genres.\n",
+    "\n",
+    "genres_expanded = steam.assign(\n",
+    "    genre=steam[\"genres\"].str.split(\";\")\n",
+    ").explode(\"genre\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "2da220a6",
+   "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>success</th>\n",
+       "      <th>total_reviews</th>\n",
+       "      <th>review_score</th>\n",
+       "      <th>average_playtime</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>genre</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Free to Play</th>\n",
+       "      <td>4346.627347</td>\n",
+       "      <td>5247.443662</td>\n",
+       "      <td>0.699454</td>\n",
+       "      <td>554.437793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Massively Multiplayer</th>\n",
+       "      <td>3468.112033</td>\n",
+       "      <td>5352.200553</td>\n",
+       "      <td>0.611893</td>\n",
+       "      <td>725.484094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Action</th>\n",
+       "      <td>1581.931446</td>\n",
+       "      <td>1938.693943</td>\n",
+       "      <td>0.709888</td>\n",
+       "      <td>144.016634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RPG</th>\n",
+       "      <td>1346.576896</td>\n",
+       "      <td>1624.963582</td>\n",
+       "      <td>0.716261</td>\n",
+       "      <td>276.985850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Animation &amp; Modeling</th>\n",
+       "      <td>1126.949367</td>\n",
+       "      <td>1199.113924</td>\n",
+       "      <td>0.738733</td>\n",
+       "      <td>88.139241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Design &amp; Illustration</th>\n",
+       "      <td>880.712644</td>\n",
+       "      <td>932.873563</td>\n",
+       "      <td>0.752982</td>\n",
+       "      <td>112.229885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adventure</th>\n",
+       "      <td>869.724083</td>\n",
+       "      <td>1102.774821</td>\n",
+       "      <td>0.713753</td>\n",
+       "      <td>151.656699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Strategy</th>\n",
+       "      <td>863.992567</td>\n",
+       "      <td>1044.205641</td>\n",
+       "      <td>0.692914</td>\n",
+       "      <td>193.130170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nudity</th>\n",
+       "      <td>822.218045</td>\n",
+       "      <td>1034.266917</td>\n",
+       "      <td>0.709080</td>\n",
+       "      <td>99.973684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Simulation</th>\n",
+       "      <td>819.659607</td>\n",
+       "      <td>986.177127</td>\n",
+       "      <td>0.659835</td>\n",
+       "      <td>154.240855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Racing</th>\n",
+       "      <td>663.785156</td>\n",
+       "      <td>802.321289</td>\n",
+       "      <td>0.667196</td>\n",
+       "      <td>142.220703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utilities</th>\n",
+       "      <td>592.404110</td>\n",
+       "      <td>658.746575</td>\n",
+       "      <td>0.688467</td>\n",
+       "      <td>112.561644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sexual Content</th>\n",
+       "      <td>571.975510</td>\n",
+       "      <td>714.738776</td>\n",
+       "      <td>0.730213</td>\n",
+       "      <td>84.395918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Video Production</th>\n",
+       "      <td>542.710526</td>\n",
+       "      <td>611.105263</td>\n",
+       "      <td>0.652966</td>\n",
+       "      <td>26.973684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Indie</th>\n",
+       "      <td>540.172236</td>\n",
+       "      <td>631.545904</td>\n",
+       "      <td>0.721458</td>\n",
+       "      <td>112.836105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sports</th>\n",
+       "      <td>519.477307</td>\n",
+       "      <td>655.620272</td>\n",
+       "      <td>0.666040</td>\n",
+       "      <td>115.518154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Early Access</th>\n",
+       "      <td>388.052471</td>\n",
+       "      <td>509.754909</td>\n",
+       "      <td>0.702389</td>\n",
+       "      <td>81.300948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Web Publishing</th>\n",
+       "      <td>338.892857</td>\n",
+       "      <td>389.642857</td>\n",
+       "      <td>0.750886</td>\n",
+       "      <td>136.214286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gore</th>\n",
+       "      <td>323.681564</td>\n",
+       "      <td>447.260708</td>\n",
+       "      <td>0.635527</td>\n",
+       "      <td>47.648045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Violent</th>\n",
+       "      <td>294.763938</td>\n",
+       "      <td>416.161329</td>\n",
+       "      <td>0.631013</td>\n",
+       "      <td>48.144721</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           success  total_reviews  review_score  \\\n",
+       "genre                                                             \n",
+       "Free to Play           4346.627347    5247.443662      0.699454   \n",
+       "Massively Multiplayer  3468.112033    5352.200553      0.611893   \n",
+       "Action                 1581.931446    1938.693943      0.709888   \n",
+       "RPG                    1346.576896    1624.963582      0.716261   \n",
+       "Animation & Modeling   1126.949367    1199.113924      0.738733   \n",
+       "Design & Illustration   880.712644     932.873563      0.752982   \n",
+       "Adventure               869.724083    1102.774821      0.713753   \n",
+       "Strategy                863.992567    1044.205641      0.692914   \n",
+       "Nudity                  822.218045    1034.266917      0.709080   \n",
+       "Simulation              819.659607     986.177127      0.659835   \n",
+       "Racing                  663.785156     802.321289      0.667196   \n",
+       "Utilities               592.404110     658.746575      0.688467   \n",
+       "Sexual Content          571.975510     714.738776      0.730213   \n",
+       "Video Production        542.710526     611.105263      0.652966   \n",
+       "Indie                   540.172236     631.545904      0.721458   \n",
+       "Sports                  519.477307     655.620272      0.666040   \n",
+       "Early Access            388.052471     509.754909      0.702389   \n",
+       "Web Publishing          338.892857     389.642857      0.750886   \n",
+       "Gore                    323.681564     447.260708      0.635527   \n",
+       "Violent                 294.763938     416.161329      0.631013   \n",
+       "\n",
+       "                       average_playtime  \n",
+       "genre                                    \n",
+       "Free to Play                 554.437793  \n",
+       "Massively Multiplayer        725.484094  \n",
+       "Action                       144.016634  \n",
+       "RPG                          276.985850  \n",
+       "Animation & Modeling          88.139241  \n",
+       "Design & Illustration        112.229885  \n",
+       "Adventure                    151.656699  \n",
+       "Strategy                     193.130170  \n",
+       "Nudity                        99.973684  \n",
+       "Simulation                   154.240855  \n",
+       "Racing                       142.220703  \n",
+       "Utilities                    112.561644  \n",
+       "Sexual Content                84.395918  \n",
+       "Video Production              26.973684  \n",
+       "Indie                        112.836105  \n",
+       "Sports                       115.518154  \n",
+       "Early Access                  81.300948  \n",
+       "Web Publishing               136.214286  \n",
+       "Gore                          47.648045  \n",
+       "Violent                       48.144721  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "genre_analysis = genres_expanded.groupby(\"genre\")[[\n",
+    "    \"success\",\n",
+    "    \"total_reviews\",\n",
+    "    \"review_score\",\n",
+    "    \"average_playtime\"\n",
+    "]].mean().sort_values(by=\"success\", ascending = False)\n",
+    "\n",
+    "genre_analysis.head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "642eae2f",
+   "metadata": {},
+   "source": [
+    "Free to play, multiplayer and action genres on top 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "e16f684a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "genres\n",
+       "Action;Free to Play;Strategy                                                          108938.000000\n",
+       "Action;Adventure;Massively Multiplayer                                                102626.833333\n",
+       "Action;Free to Play                                                                    93925.475000\n",
+       "Action;Free to Play;Indie;Massively Multiplayer;RPG;Simulation                         80360.000000\n",
+       "Action;Adventure;Free to Play;Massively Multiplayer                                    55847.500000\n",
+       "Nudity;Violent;Gore;Action;Adventure;Indie;RPG;Simulation;Strategy;Early Access        30363.500000\n",
+       "Action;Adventure;Indie;Massively Multiplayer;RPG                                       29585.846154\n",
+       "Adventure;Casual;Free to Play;Massively Multiplayer;Simulation;Sports;Early Access     25818.000000\n",
+       "Action;Free to Play;Massively Multiplayer;Simulation                                   25491.500000\n",
+       "Massively Multiplayer;RPG                                                              21763.500000\n",
+       "Free to Play;RPG;Simulation                                                            21481.000000\n",
+       "Action;Adventure;Casual;Free to Play;Indie                                             20951.785714\n",
+       "Action;Adventure;Massively Multiplayer;RPG;Simulation;Strategy                         19493.666667\n",
+       "Action;Adventure;Casual;Free to Play;Indie;Massively Multiplayer;RPG;Simulation        19114.000000\n",
+       "Action;Adventure;Indie;Massively Multiplayer;RPG;Simulation;Strategy                   17255.000000\n",
+       "Action;Massively Multiplayer;Simulation;Strategy                                       17201.000000\n",
+       "Action;Free to Play;Massively Multiplayer;Early Access                                 16625.666667\n",
+       "Action;Adventure;Casual;Free to Play;Indie;Massively Multiplayer;RPG                   15666.000000\n",
+       "Action;Adventure;Free to Play;Indie;Massively Multiplayer;RPG                          15071.000000\n",
+       "Action;Adventure;Casual;Free to Play;Massively Multiplayer;RPG                         13900.000000\n",
+       "Name: success, dtype: float64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets have a look at genres without splitting them:\n",
+    "\n",
+    "steam.groupby(\"genres\")[\"success\"].mean().sort_values(ascending=False).head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab1dacd1",
+   "metadata": {},
+   "source": [
+    "### H6 Visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "05a53afa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+nXasrfyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw19JkiRJkiRJakCGv5IkSZIkSZLUgAx/JUkN7bLLLgsTTTRRe89GQ+rSpUsYPHhwe8+GJEmSJKkMw19JUofy9NNPhzHGGCP07du37t+dYYYZwumnn1703MYbbxzeeeed0NqOOOKIGIbmHw8++GBoS3369An9+/cf5ek88sgjRcsxxRRThH79+oVhw4aFtvTrr7+GQw45JMw888xh7LHHDpNNNllYbrnlwm233VZxvY8KQ21JkiRJjaJbe8+AJElZF198cdhzzz3jv5999lmYeuqpR2mAxhlnnPhoC7179x4p7J1kkklGet+ff/4ZxhprrDaZp1H19ttvh/HHHz+8++67YaeddgprrbVWePXVV2NA3xZ22WWX8Oyzz4azzjorzDXXXOHbb78NTz31VPy3Hv/8808Mdbt29by3JEmSpNGH34AkSR3Gzz//HK6//vqw6667xspfWjbk3XHHHWGRRRaJVaCTTjppWG+99QoVrx9++GHYZ599CtWq5do+nHvuubGSlAB29tlnD1deeWXR6/zuRRddFKfdo0ePMOuss4bbb7+96vx369YtTDnllEUPPmObbbYJ6667bjj22GNjmM1n4uOPPw4bbbRRnD9C4nXWWScMHz68aJrMx5xzzhmXd4455gjnnHNO2c/ncx599NFwxhlnFMYgTY/nF1100dC9e/cw1VRThYMPPjj8/fffVZdp8sknj+9fdtllw+GHHx7eeOON8N5775V870EHHRRmm222OGYzzTRTGDBgQPjrr7/ia8wHwesLL7xQ9DtU7E4//fTh33//LTlNxv0///lPWGONNWKF70ILLRRPDmy33XY1rXd+n9CY5f7oo4/C888/H1ZeeeW47Uw44YSxivjFF18sfB6fAdY900o/g2rjBRdcMK4Llu/II48sGsO33norLL300vF1PpMTAdkq4hVWWCHsscceRcv39ddfx23koYceqrouJEmSJKlehr+SpA7jhhtuiAEn4egWW2wRLrnkktDU1FR4/a677oqhHEHgSy+9FAMzAk3ccsstYdpppw1HHXVU+Pzzz+OjlFtvvTXsvffeYb/99gtDhw4NO++8c9h2223DkCFDit5HsEcwS5Urn7f55puH7777rvA6oSCtHmrFvFJF+8ADD4Q777wzhqKrrrpqrKp9/PHHw5NPPhnGG2+8sNpqq8XKYFx99dUxcCU0fvPNN8Nxxx0XA9XLL7+85GcQ+i6xxBJhxx13LIxBr169wqeffhqXgdD8lVdeieE3ldXHHHNMqEeqoE7zl8eyELoSEDMvF154YTjttNMK47XSSiuFSy+9tOh3+JnQOlXk5seVAP3uu+8OP/30U8nPrLTeaRlxwgknxAD99ddfj0E209l6663DE088EZ555pkY7DM2afqEw2m+mFb6mXW01VZbxW2H5Tv//PPjsrJuUmUxAT/BN5XKF1xwQTj00EOL5nWHHXYI11xzTfjjjz8Kz1111VVhmmmmicFwHu8bMWJE0UOSJEmS6mH4K0nqMAgkCX1BCPrjjz/GitWEoG2TTTaJwSzVsPPNN1/sBwsqZ2lFQACZqm5LOfnkk2PYuNtuu8Uq1X333Tesv/768fks3rPpppuGWWaZJYauVCU/99xzhdepHKZ6NOu1116LAW56pGAa4447bgwhaQ3Bgwpnql15bp555onLQ+BIdSr9djFw4MBwyimnxPmbccYZ479UuBI8lkIlK1WkBJBpDBgTqoUJgc8+++wYrhNSMoZMu1zFbR5BKGNEUJkql/MOO+ywsOSSS8YAl/YQ+++/fwz0s+HntddeWwg/qbhlzAjfy40rISptHnr27BnDa5afoDyptN4J2Fl25ol5ZlwIWdnGGAfGnOkTEqftjJ7CoGqYaaWfGS+qpQmOqfqlevjoo48urAtC/ffffz9cccUVcbukAjgFwwnrD9l+xQTIbGupYjnr+OOPj+s0PViHkiRJklQPw19JUodAVSzhKoFraqHAzdoIhJOXX345rLjiiqP0OVTQLrXUUkXP8TPPZ80777xFwe0EE0wQvvrqq6JK3vwl/ASMzGN63HzzzYXXCHizfX6pwKV9AqFlCosJMn///fcYIv7yyy/x3+23374oUKZal+frXWYqgrMBI8tMoP3JJ59U/F2qall+2lUwTyxTuX7FBNpMl9CUeSUMJsxOCJ0Jaqm+TsHn8ssvX9RaIT+utJvgJnM8v8EGG8QK3mWWWSYGr9Uwn9n1iC+//DJWRlPxS6DKemUcsvNZCuuL6uLsukgV1oTHbL+Es9nwORv+g3YQW265ZaxoT+E31eeEv6VwYoMTIOlBmxBJkiRJqoc3fJMkdQiEvPRPzd7gjZYP9GqlYpWgrq1u3IYxxxyz6GeC02pVsoSNVAqXQoCaReBI/1paO+RRbcrroHXCYostVvR6W91sLbU7ICClZQJBdTlPP/10bI1BhSztLFhf1113Xawuzo4PrROocKYKlhYItIeoZV0Q+PKgrzABOEEs/13pxnlsL/mKWip3uVkcn0uvYbYvgvFyrSwS1gfLlqp386Furah+nn/++WPozjhQicx8lMK88ZAkSZKk5jL8lSS1O0JfLpcnKFxllVWKXqNalFYBu+yyS6zipAI02yYgiyCQ3quVcKk/bQMIARN+5gZdbYkbh1EpS6hKuJpHeEoQTtUroWqtSo0By0zFLmF6CkNZZsJcKnsrod1E/oZ5pdCagRAz2+eWG7GVCj/nnnvu2I6B9V4qTK2GdcXvUiXN8tay3hOWm8+mzy+opv3mm29GCpvz02N9Ud1bLtyn6ptpUVk8xRRTxOdSv+AsKsAXXnjhGOoTfnNiQ5IkSZJai20fJEntjhugff/997HFAcFg9tGvX79C6wd64BIE8y+tDOgXyw29EtoHPPbYY/EGZ/lALznggANiuwFuevbuu++GU089Nd40jP60bYlAl96266yzTqyu/eCDD2Kv37322qvQioFKU/q+nnnmmeGdd96Jy0u1KPNcDmPADceGDx8ex4BqZfobE0zuueee4a233oo9ZxlD+h2nG62NKtoo0DqBal/aUjDPqb1DPohefPHFY9UuLT6qVXP36dMn9tX93//+F5eJm7/95z//ie0iUmhey3rPzueVV14Ztx/GifWQnwemx0mGL774Im6X4MZ7nKBgndB6gt9nWWltAXoA06+YkwrcJJCQOb2Wrz4mAB80aFAM47mBoSRJkiS1FsNfSVK7I9xdaaWVYrVrHuHvCy+8EAM1gsAbb7wx3H777fHSeS6Zz96EjVYABISEcOlGXXlUEnPJPzcv48ZrBIsEqky7HgSERxxxRGgubj5GYDnddNPF6ldCUcJvqllTqElIyA3hmD8qRpdbbrkYXFONWw4hNm0hqI5lDAhkuUkboSljxc3IqKLms1I42RLWXnvteDM2+vWybqgEHjBgQMn38tm0Wdhuu+2qjistJC6//PJYEc4YEWDzXPZGcrWs9+y2RqBLJS/9dwnbqb7OogKdG7jRw3eBBRYozAcnKe6///544zkC7NNOO63QsoExHzx4cGwPweusu1QFnW8LQehNT2v+radlhCRJkiTVq0sTZSeSJKlm3OCrZ8+e4Z577qk7NFaIN2sjxCfQb+Rxpfp36aWXjjf2I5hOUlBNWwhC6FqNGDEiniDp1f+G0LV7j1aaa0mSJEl5wwf1DR1J+m7AjaFLtRHMsuevJEl1GjJkSKw6boSAsi1RFUvwSZ9bbtrWaONKm4vxxhsvtpYg8N17773DUkstVQh+//rrr3izOSquqRyuJ/iVJEmSpOaw7YMkSXXq27dvuOuuuxy3OtESYqGFForhbqmWD519XH/66aew++67hznmmCNss802sf0D/ZWzlcBTTTVVrPg977zz2nVeJUmSJI0ebPsgSZLUCdj2QZIkSWofwztx2wcrfyVJkiRJkiSpAdnzV5IkqRMZeuSqVc/uS5IkSRKs/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkiRJkiRJkhqQ4a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSZIkSZIkSQ3I8FeSJEmSJEmSGpDhryRJkiRJkiQ1IMNfSZIkSZIkSWpAhr+SJEmSJEmS1IAMfyVJkiRJkiSpARn+SpIkSZIkSVIDMvyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw19JkiRJkiRJakCGv5IkSZIkSZLUgLq19wxIkiSpdnMPvC907d7DIZMkSVKnN3xQ3/aehYZn5a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSR3YZZddFiaaaKLQUXX0+RsdPPLII6FLly7hhx9+aLFpbrPNNmHdddcNnXFbYywGDx7cruMnSZIkSR2F4a8ktaCnn346jDHGGKFv3751/+4MM8wQTj/99KLnNt544/DOO++EtvLJJ5+EscYaK8w999yhI6s34GuteeDxzDPPFD3/xx9/hJ49e8bXCBZb25JLLhk+//zzMOGEE4aOpLXH54gjjgjzzz//SM8zFquvvnqzpytJkiRJjcTwV5Ja0MUXXxz23HPP8Nhjj4XPPvtslKc3zjjjhMknnzy0ZXXlRhttFEaMGBGeffbZ0Oj+/PPPUfr9Xr16hUsvvbTouVtvvTWMN954oa0Q1k855ZQxTO1o2mN8GIvu3buHzuavv/5q71mQJEmS1IAMfyWphfz888/h+uuvD7vuumus/CVIzbvjjjvCIossEsYee+ww6aSThvXWWy8+36dPn/Dhhx+GffbZp1AxWe5S93PPPTfMPPPMMfSbffbZw5VXXln0Or970UUXxWn36NEjzDrrrOH222+vOv9NTU0xqNtyyy3DZpttFoPsPOZnuummi9Nl+t9++23hNSqU+ey33nqr6HdOO+20OL/J0KFDY2UmAeAUU0wRP++bb74pvM5Y7LXXXuHAAw8Mk0wySQzzqPLMVkiDz+fz0s+lWhX0798/Ti877T322CM+z/ivuuqqNc1TOVtvvXW47rrrwm+//VZ47pJLLonP5x100EFhttlmi2M300wzhQEDBhQFfq+88kpYfvnlw/jjjx8mmGCCsNBCC4UXXnghvsa2sdZaa4WJJ544jDvuuKF3797h7rvvHqltAaE9JwzuueeekQJXpvvrr7/Gnz/++OMY8rNtMcbrrLNOGD58eMllvOKKK2KlLhW7WYw149QS41Oq9cLLL78cnys1X2yHRx55ZByztL+k/S1bFc7v8jPzQIU0+x1V7Y8++mjZeWab3nTTTcM000wT19U888wTrr322rrH47bbbgsLLrhg/EzWN/P7999/F15nvtiX11577bhOjz322IpjKUmSJEnNYfgrSS3khhtuCHPMMUcMZLfYYosYchGoJnfddVcMLNdYY43w0ksvhYceeigsuuii8bVbbrklTDvttOGoo46Kl63zKIUQb++99w777bdfDCx33nnnsO2224YhQ4YUvY+giXDv1VdfjZ+3+eabh++++67wOoFpNlAF0yAcXGmlleL8E5j98ssvhdepBN5+++1jeEowR1B5zDHHFF4n2Fx44YXD1VdfXTRdfiZMBuHeCiusEBZYYIEYbN57773hyy+/jPOadfnll8dAjM888cQT47g88MAD8bXnn38+/ktQzTiln2vFtAnOn3zyyXDeeefVNE8Ei6UqawloGcubb745/vzRRx/Fqu9SoSjhK9N54403whlnnBEuvPDCGIwnrCO2AZbnf//7Xzj44IPDmGOOGV/bfffdY9jItF977bVwwgknlKyeJTRec801wzXXXDPSOiCcJMwkcCb0Zn4ef/zxOA5Ma7XVVitZCb3hhhuGf/75p+gEwldffRW35+22267iWNczPvWgHQr7ACF42l94rpwDDjggvp/9bokllohBevbERdbvv/8e55vlYx/baaed4vw+99xzNY8H47rVVlvFfZX1ff7558d1nw942Qc5JrBOS40l65xAP/uQJEmSpHoY/kpSC6FSltAUBGk//vhjUYUhwc8mm2wSg9k555wzzDfffOGQQw6Jr1F9Sa9gAjkqXXmUcvLJJ8cK19122y2Grfvuu29Yf/314/NZvIfqxVlmmSUcd9xxsSo5hVegEpfK1/z8M3/MB9WRVCveeOONhdcJLFkuKnL5bKpzU+VsNsDMVklSDUyQyfM4++yzY8jKPBGU89+E5ATP2d7G8847bxg4cGCsWiZEI1QmLMdkk00W/6VqlXFKP9eKaRIoE9LzqGWe6KfLe0shtOP9IOAjbC81T4cddlisPiUMJXzcf//94wmDhGCU4J15YB4JGdlG0mtLLbVUrEJlvRDwLrvssiXnh7Gm8jVV+RIYEkymdUB1+r///hurw5ke2yJBOp9RqgcvlcSE99n2DVdddVWsAM9WVZdT6/jUg3kisO7WrVthf+G5cjhh0a9fv7isVNuyPktVtoOKX9YN/YQZa9q4sN2ndVXLeLCPE95T4cw0Vl555XD00UfHEDiL6XDyhvfw+3nHH398nNf0oI2GJEmSJNXD8FeSWsDbb78dw1UCVxBKUYmYDZioll1xxRVH6XPefPPNGAJm8TPPZxGeJlTQUhFKdWJCkEogllD9SvVxCq/Bf2fnn89YbLHFij6HKsoswmMutU83+aLilEvfCTTBZfqEqgR36ZFee//990vOP6aaaqqi+R8VVHVm1TJPVGfm21lkx4kb/Q0bNiyGm+WqYQldWVcElXwGYTCBa0KQv8MOO8QAeNCgQUXjQdBOlTW/TyhORXc5hKtUDKfKVKpuWf9MNy3ve++9F080pOXl5AMVr9nPzNpxxx3D/fffHz799NP4M8vJCYZa+gzXOj6tKbudsm9yMiG/zyRU9RLUEowzLozPfffdV7Suqo0HY0y1enab4neoUE6hPJiPSjg5xEmk9KBdhyRJkiTVo1td75YklURISj/PqaeeuvAcLR+48RSVpVTtVapMbGmpXUBCKEW1Zzm0CSD8y4a7zD+/Q/Urlb61INikhQLTW3zxxeO/9EBOqECm6pW2BXkEvM2df3Tt2rWozUa5m2gRhmfVOk/l0P+VSlxaYjCG9A7+6aefit5D+EnlLRWhVEuzPdBW45RTTilqAUAlKFW69Owl5OU9BM+EwvwerxE6UhHK71KVmkdLiw022CCOPWE8/3IigtAzLS8BeL49B8pV5FINTRUy/W5XWWWV8Prrr8d5qUUt48O6Q3b9tdcN0E466aRY5X766afHAJjthR7R2ZYY1caDMWZdU5WfRw/gcttiHsePznjzOkmSJEkdh5W/kjSKCH0JgQjjqO5ND6r/CINTGwSqWVPrglII7ag6rITL1unRmsXPc8011yiH1/REzc//MsssU7hkn8+mB29WqvDNIuSkyjVVexJAJlQBE5TR+oCWFNlHtSAsi3A4P1YEl/leySxHNS0xT1Sz0jKBFhW0zch76qmnwvTTTx8OPfTQWO1JWwdu4pZHyM5N/wh4CQ6zrQW45H+XXXaJFdqsK3oGl8M6oHcxy/Xwww8XWj6k5X333XfD5JNPPtLyEkqXQwBNhSvzRBVxPS0Iqo1PCp2z66/auqtlfym1nbK/0oqE7bkU9idugEfFMgEvLRmyLUlqGQ/GmKsB8uPLIwXdkiRJktQW/AYiSaPozjvvDN9//32sbKRXbvZBn9HUOoFKToJg/uWS83TjroTwkZthcSn5N998U/bGVQRO9C0lwDv11FNjGEiP0uYiZHvxxRdjmJWff9pYcIM0AjNaDxAo0l+Yz6aimZ/zCC2p7KTil5vCZauhuXEZN55jutzYjDYDXFJP39Nag7w0VgTpX3zxRRx7UHHMDdsI4pk/xpkbdlXTEvNET9ivv/46XupfCmEvbQOo5GX6Z555Zrx5X/Lbb7/FNhwEpITCBJDMSwooqTxlnj744IO4rmhTUS68BP2AqcIm9J1xxhmLKrp5jn7PBJzcmIxp8rms308++aTsNKlK5nVC53pbN1QbH0JRwlOqn1l3VNFmq6LLbQPMO9sv+ws3Ryvnv//9bxxvWnewvtlmyi0D64qbCxLYs59yU0VuAFjPeBx++OFxO6T6lwCe6bDuafUhSZIkSW3J8FeSRhHhLpV/paomCX8JJOnRys2guIEavVi5mRRhZfYmbARj9MvlZmzlLr9fd9114yXpBLC9e/eON5Ci8rCWG2/lgzOCtjT/VA6nPrdZtByg1+7dd98d2zgQdPH5VERSnVoqzKKXLG0UqBzOVpyCIJhgk1CVy+W5rJ5gk5u31VMRSTBIQEdgyCX4oC3CgAED4g3pFllkkRhAU2laTS3zROBeqb8trxGoUo1aytprrx0regl4WfcEi8xrQjXst99+G+eX6t+NNtootkcgPATzRmhJ4EuQynvOOeecivNDmF1qHfTo0SOeZOAGYwT1TDO1ZKA3cDls32zP9K9lO6xHtfGhkpsTI4SzVMhzUoQex5UwL4wFJxjYX7I3GsyjhzIPttsnnngi7oP5Gx4mbNNU7rI9sV8Ropda3krjwe9yUoh9hG2Rfee0006L1d+SJEmS1Ja6NOUbJEqSGho3nKIPK31l6w2NR1dUET/66KOxQnZ0xg0LOelA5XJnwMkUKp9feumlGLp39vEYMWJEDJ179b8hdO3eo00+U5IkSWpNwwf1dYBH4bsBN4auVMQDb/gmSaMZWgZQdWzwWzuCctpcjK5ok0DwzaNSxfHowvGQJEmS1FkY/krSaKZv377xodpl23OMjmitQeBJO4bZZ589jO4cD0mSJEmdheGvJEmq2j6hM6K3dWt0t+qs4yFJkiRp9OMN3yRJkiRJkiSpARn+SpIkSZIkSVIDsu2DJElSJzL0yFWr3tFXkiRJkmDlryRJkiRJkiQ1IMNfSZIkSZIkSWpAhr+SJEmSJEmS1IAMfyVJkiRJkiSpARn+SpIkSZIkSVIDMvyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw19JkiRJkiRJakCGv5IkSZIkSZLUgAx/JUmSJEmSJKkBGf5KkiRJkiRJUgMy/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkiRJkiRJkhqQ4a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQG1K29Z0CSJEm1m3vgfaFr9x4OmSRJUoMaPqhve8+CGoiVv5IkSZIkSZLUgAx/JUmSJEmSJKkBGf5KkiRJkiRJUgMy/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkqQGdtlll4WJJpqovWejIXXp0iUMHjy4vWdDkiRJksoy/JUkqQN5+umnwxhjjBH69u1b9+/OMMMM4fTTTy96buONNw7vvPNOaG1HHHFEDEPzjwcffDC0pT59+oT+/fuP8nQeeeSRouWYYoopQr9+/cKwYcNaZD4lSZIkqS0Y/kqS1IFcfPHFYc899wyPPfZY+Oyzz0Z5euOMM06YfPLJQ1vo3bt3+Pzzz4seyy677Ejv+/PPP0Nn8fbbb8f1cOONN4bXX389rLXWWuGff/5p79mSJEmSpJoY/kqS1EH8/PPP4frrrw+77rprrPylZUPeHXfcERZZZJEw9thjh0knnTSst956hYrXDz/8MOyzzz6FatVybR/OPffcMPPMM4exxhorzD777OHKK68sep3fveiii+K0e/ToEWadddZw++23V53/bt26hSmnnLLowWdss802Yd111w3HHntsmHrqqeNn4uOPPw4bbbRRnL9JJpkkrLPOOmH48OFF02Q+5pxzzri8c8wxRzjnnHPKfj6f8+ijj4YzzjijMAZpejy/6KKLhu7du4epppoqHHzwweHvv/+uukwE57yfEPvwww8Pb7zxRnjvvfdKvveggw4Ks802WxyzmWaaKQwYMCD89ddf8TXmo2vXruGFF14o+h0qtaeffvrw77//Vp0XSZIkSaqX4a8kSR3EDTfcEANOwtEtttgiXHLJJaGpqanw+l133RUD2TXWWCO89NJL4aGHHoqBJm655ZYw7bTThqOOOqpQdVvKrbfeGvbee++w3377haFDh4add945bLvttmHIkCFF7zvyyCNjMPvqq6/Gz9t8883Dd999V9RiglYPtWJeqaJ94IEHwp133hlD0VVXXTWMP/744fHHHw9PPvlkGG+88cJqq61WqAy++uqrY+BKaPzmm2+G4447Lgaql19+ecnPIPRdYoklwo477lgYg169eoVPP/00LgOh+SuvvBLDbyqsjznmmFBvFXWlymWWhbCdgJh5ufDCC8Npp51WGK+VVlopXHrppUW/w8+E1gTDeX/88UcYMWJE0UOSJEmS6tGtrndLkqRWQyBJ6AtC0B9//DFWrFLVC0LQTTbZJAazyXzzzRf/pXKWXsEEkFTclnPyySfHsHG33XaLP++7777hmWeeic8vv/zyhffxnk033TT+N6HrmWeeGZ577rk4X6BymMrjrNdeey0GuMlcc80VfwfjjjturOKlEhhXXXVVrHbluVSlTBBKFTD9dldZZZUwcODAcMopp4T1118/vj7jjDPGYPX8888PW2+99UjLNuGEE8bpU3mbHQOqhQmBzz777PhZBOy0cqBSl3C5VPCaR5DMGE0zzTSFyuW8ww47rPDfhL37779/uO6668KBBx4Yn9thhx3CLrvsEk499dRYgfziiy/GMbvttttKTu/4448vWteSJEmSVC8rfyVJ6gCoiiUoTYErLRS4WRuBcPLyyy+HFVdccZQ+hwrapZZaqug5fub5rHnnnbfw3wS3E0wwQfjqq6+KKnn32GOPot8hFGUe0+Pmm28uvDbPPPMUgl9QgUv7BMJqAmMeBNi///57eP/998Mvv/wS/91+++0Lr/OgWpfn611mKoJTyJyWmTYbn3zyScXfpZqa5addBfPEMmWXI4uWHUyX4Jl5JQz+6KOPCq/T+oKAnuprUCVM4E5QXMohhxwSTwCkB20yJEmSJKkeVv5KktQBEPLSg5aQMaHlAxWiVKxS1ZraDrSFMcccs+hngtNqfWkJRWeZZZaSrxGgZhG8LrTQQrG1Q95kk00WXwetExZbbLGi1wlQ2wotKQi+6f1LUF3O008/HVtjUKlLOwvWF1W/VC5nx2errbaKFc5UM19zzTWxPUQ5rHsekiRJktRchr+SJLUzQt8rrrgiBoW0O8iiWvTaa6+N7QKoxqXilh69pRAu/vPPPxU/i5un0V832zaBn2nR0JYWXHDBWClLqEq4mkd4ShA+bNiwGKrWqtQYsMxU7BKmp+pflpkwl8reSmg1kb9hXilPPfVUvHHboYceWniOG/Dl0fph7rnnjq0oWO+ppYUkSZIktQbbPkiS1M64Adr3338fWxwQDGYf/fr1K7R+oAcuQTD/0sqAfrEnnHBCYTq0D3jsscfiDc6++eabkp91wAEHxHYD3PTs3Xffjf1nuVkc/WnbEoEuPYPXWWedWF37wQcfxF6/e+21V6EVA1W09L2l3/A777wTl5eqWea5HMbg2WefDcOHD49jQLUy/Y1pmbDnnnuGt956K/bYZQzpd1xLv99azDrrrLHFA9W+tKVgnlN7h3wQvfjii8d+w7T4aMtqbkmSJEmjH8NfSZLaGeHuSiutFKtd8wh/X3jhhfDqq6/GG7/deOON4fbbbw/zzz9/WGGFFQo3VMNRRx0VQ09uxkbrhFKoJKbVADcv6927d7x5GoFquqlcrQhZjzjiiNBc3JSNoHq66aaL1a+EooTf9PxNlcBUyXJDOOaPnsHLLbdcDK6pxi2HEJu2EFQyMwYEstyk7e67745jxQ3yqKLms7I3aBtVa6+9dthnn31iH2TWDZXAAwYMKPlePvvPP/8M2223XYt9viRJkiSV0qWJayAlSZJq9Ouvv4aePXuGe+65p+7QWCEcffTRMcQn0K/HiBEj4gmCXv1vCF2793AoJUmSGtTwQX3bexbUwaXvBtwYulQbvSwrfyVJUl2GDBkSq44NfuvDTeyGDh0ab+BHCwpJkiRJam2Gv5IkqS59+/YNd911l6NWJ1pCLLTQQjE0t+WDJEmSpLbQrU0+RZIkaTRHv2IekiRJktRWrPyVJEmSJEmSpAZk+CtJkiRJkiRJDci2D5IkSZ3I0CNXrXpHX0mSJEmClb+SJEmSJEmS1IAMfyVJkiRJkiSpARn+SpIkSZIkSVIDMvyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw19JkiRJkiRJakCGv5IkSZIkSZLUgAx/JUmSJEmSJKkBGf5KkiRJkiRJUgMy/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkiRJkiRJkhqQ4a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSZIkSZIkSQ3I8FeSJEmSJEmSGlC39p4BSZIk1W7ugfeFrt17OGSSJKnNDB/U19GWOikrfyVJkiRJkiSpARn+SpIkSZIkSVIDMvyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw1+pissuuyxMNNFEjlMr6NKlSxg8eHCnGNsZZpghnH766Z1y3qsZPnx4XJ6XX365xaZ5xBFHhPnnnz90JI888khczh9++KGudd0e4zc6aqtjba3bQVtNR5IkSZJak+GvGs7TTz8dxhhjjNC3b9+6f7dU6LPxxhuHd955J7Q2wjKChPzjwQcfDG2pT58+oX///qM8nRSMpMcUU0wR+vXrF4YNGxbaUmuFkJ9//nlYffXVR2kabG+MzXXXXTfSa717946vEYi1tl69esXlmXvuuUNH0trjUy5sfP7558NOO+0UGgXLmfbDrl27hqmmmioe1z766KOR9v30vrHHHjvMNddc4Zxzzil6z59//hlOOumksOCCC4Zxxx03TDjhhGG++eYLhx12WPjss88qzsejjz4aVlhhhTDJJJOEHj16hFlnnTVsvfXWcZpteaxtqePikksuGfcbxkCSJEmSOirDXzWciy++OOy5557hscceqxpG1GKcccYJk08+eWgLBFqECdnHsssuO9L7UljSGbz99ttxPdx4443h9ddfD2uttVb4559/Qmc35ZRThu7du7dI8HrppZcWPffMM8+EL774IoZrbYGTJSxPt27dQkfTHuMz2WSTxXCys6l0XJhgggni8eTTTz8NN998c9wvN9xww5Het+OOO8b3vfHGG2GjjTYKu+++e7j22mvja3/88UdYeeWVw3HHHRe22WabeIx97bXXwplnnhm++eabcNZZZ5X9fKa32mqrhYUXXrjwe7x/rLHGKhwP2vJY2xKYd/YbwnJJkiRJ6qgMf9VQfv7553D99deHXXfdNVb+lqoKvOOOO8IiiywSK9smnXTSsN566xUquz788MOwzz77FKrfylUHnnvuuWHmmWeOX/5nn332cOWVVxa9zu9edNFFcdqpwu3222+vOv+Eb4QJ2QefQdCy7rrrhmOPPTZMPfXU8TPx8ccfx4CG+aOabp111omXoGcxH3POOWdc3jnmmGOkSr4sPofqvDPOOKMwBml6PL/ooovGwJPKwYMPPjj8/fffVZeJMIf3E2IffvjhMQR67733Sr73oIMOCrPNNlscs5lmmikMGDAg/PXXX/E15oOqxRdeeKHod6jUnn766cO///5bdV7SMjKWJ598cpyvnj17xoArfQ6++uqrGFITRs0444zh6quvHmk6+bYPtayLUjbffPM4tvx+cskll8Tn82HsqaeeGuaZZ54YehKK7rbbbnGbT9h+me+JJ544voeTCXfffXd87fvvv4/TJNhkudgmU6iabVvAOE477bRxG8966aWX4vjzGeBS9x122CFOj2CRis5XXnml5DIS9o055pgxsM2iknKZZZZpkfEp1XqBeeQ5qtDzeG7bbbcNP/74Y2Fbp0q8XIsPxoNKb8aObfOmm24qO8+Emdtvv33cdng/+yv7VL3j8cQTT8SfmQbre6+99gq//PJL4XXm8+ijjw5bbbVVXAeVqpVZBo4nbPNUrDJ/zz33XBgxYkTR+9j3eB/LyHhkj12nnXZanKeHH344zstCCy0UpptuurDccsuF8847L4bC5dx///1xuieeeGKsMOf4SRh84YUXxuUrdaxNVfusbz5nvPHGi9s848t0mB7HF46Lzd0O8O2334ZNN900TDPNNHH52cdS4F3puFiq7QPBOvsdx0nWzymnnFL0WTzHOG233XZh/PHHj8t1wQUXlB03AnfWUfYhSZIkSfUw/FVDueGGG2LASdiyxRZbxNCgqamp8Ppdd90VA9k11lgjhlkPPfRQDDRxyy23xNDrqKOOKlTdlnLrrbeGvffeO+y3335h6NChYeedd44h0pAhQ4red+SRR8Yw8NVXX42fR1j13XffFYUAKWyqBfNKtd4DDzwQ7rzzzhhWrrrqqjFAePzxx8OTTz4ZwxEClVQBSGhJ4Eo48uabb8bQgUD18ssvL/kZhBtLLLFEofqPB6ET1YIsA6E5AR9BGBXWxxxzTKhHCnnKVSiyLARABMTMC8EQgVMar5VWWmmkKlB+JpwhmKx1XFlX77//fvyXseAzsycKmB5hI68T8hGYEwiXU8u6SEFRPhCmHQa/m9bJr7/+Gk9gEA7lsYxUWVJBzfsJ4Q488MDC64TYhEWpsvKEE06I8wHWO+N6zz33xG2BdcjJj1KfQRB2zTXXFD3PtrTUUkvFoB1UjTImTO9///tfbAOw4oorFm3jCcE/YWL2JAljxjRLLWdzx6ceBKAEvKkilsf+++9f9v2MH21L2P7ZlzfZZJM4jqWkAJ1qd8acffA///lPPD7VOh5sn2w/fCbHEJaZ4HWPPfYo+ixOYtB2geMZ85hOZLENl8N64zhGxTePavts2oYJRKn8XWCBBUq+N1sBm1pNJAS1jDHbZj0YB7axe++9N34+xx1O7H3yyScxkGUbp+XEs88+G5rr999/j0E2fx84phOib7nlljEcr3RczGM/4JjPtsH+x3GIdZI/CUkgTAU064wwm5OVHNtLOf7442NbifQo9bmSJEmSVInhrxoKwQChLwhOqOojIEgIQfliTjBLNSyhySGHHBJfo1qTIIQAL1XdlkLYQrDCl3aqVPfdd9+w/vrrx+ezeA8h2iyzzBJDVyo0U5gAKt/y4RuBAWFdeqRgGlRyUsVLVRkPwiBCJp6jUo3lIQilj2eqcBs4cGAMGpg/qhD5l8rm888/v+SyES5QaZyq/3gwJoSfhA5nn312DNepnGUMmXatFbcEJowR1XWpcjmPEIdQjgCXClbCuBSYgUpTAiACTrz44otxzAjfK41rHpWxaVnWXHPNGCYRroOeo4RNBM+LL754DIXYrn777bey06tlXTCmLDcVn3kEfgREnKggbGYZSvUppjJ0+eWXj+NDpS3he3Z8+DwCWuaBcJFlS21DeI3QjtApBemMcSmEmwTYqScsy0bfXZ4HISTbMuEm06M6lHVL1Wa5ilgqTbPBPRX4hG6EZdXUOj71YDtne08VsTxSUF4KYTfbH/s81bYsd7k2B6xj9g/ew37HuLGNZtdVtfEg9OP3WOeML/sFwf8VV1wR35ewHXAiijHhAapJqfDN4ljI8nEcIVDnxAYnC8q1zqC69qqrrorBM5+R9o38vsvJtHS8Yh4Txjb7XsaP4yFVwswbv8c+WK2SlW2Pk3j0H2Z7ZfsnKCW4Z/qMK//mT77Vg2MSxxq2KfYb2gbx9yOtr3LHxTwq8zkBQuDLdsLfAMJ6eiRncSKNvx/8beBqB45X5eafv0+su/TIVsBLkiRJUi0Mf9UwCAQIpAgYwCXh3ECI4C7hUmC+nI8Kqv0I2LL4OV8FOO+88xb+m4CFCsNs9ShhY76KjxCDeUwPLiFOCPQIIBIqEGmfQFidwhcCbIIhquW4PJx/CZmygTKBIc/Xu8xUvmUr+VhmAm0q8CqhApLlp10F88QyZZcjH6Iy3RTEEQZnb0pF6EzoQtUiCARTGFppXPMIz7PhDWFUWjcsK9sOoW9CSFzqxmC1rgsQ5L/11lsxaMojfGYsqYok6CpX1crN/9h+mQafRXUil6xTDQsuxWf9MoYE/wR3CdWFBLgEXFQLP/XUU2WXh/cQYKfqX06gMD6pRyzLy/zSMiO7bX3wwQdlty2CMMaIfr1p3RF01tK3t9bxaU1s//mfy1X+4r///W/chmiLwdhwaX92W642Howxz2XHlwpowlDGOSFgziMgJjzOYnvhmELbFE7aUKmdbZeQcKKHz6Lil0pXThax7ZTD+5ku6yRthyDcZXtP2N8Iuzle0LKBbZiTYqnPeTns28x7QnBNEJwq/dNzlSrzqyHoJtDnGMt+y/Lfd999I90Qr7l/G959992iPufZvw3p5EO5+ad9BH87sg9JkiRJqkfHu7uP1EyEvPSgJWRMqBTkyzMVZlRvpbYDbSFf4cmX/GpVsoSiVIOVkg/JCMMIl0r1oyVwSr1gqWBdbLHFil6vdql3S6INAoEFvTmzIU7e008/HSsdqZgk5GJ9EVZme2YyPvQ3JUSiiplwMttLtTXXTSXV1kU1hM0EuQS2XL6ewu0s2kVQyUsQR2hHSEUFLuE+l+VTlUhlKmPH5ev0WCUAZPyoZKRfLf166QFM6xBCZCo/8xXrCeuC8aW3M/9SCUnYm5aXwLxUD9VyITnrn8pN1h3VsFRXl+vB2pzxSWFgts1Lto9zW2K7pZKUsSckZrun+jPbmqDaeDDGtJQh0M+jsjep9aZ3jE86thDsE9KzLeX7lbPeDz300HisZB1nQ1YqkPPtCVKFMdtjLQh9WZc8CFypkKVfMPt9rftqpf23OdsB64bjCNXEqac2FdetdWPNlj7+SJIkSVIlVv6qIRD6Uu1G2JKtnKV6jjA43byHiqt0eX8p2TvPl0NwwiXxWfxMNVpbonKPijJCJEKd7IPglGo4ln3YsGEjvU7YVM8YsMyEs9lAhWUm1KKytxI+i8vRKwW/oBKVfrIET6mVQLq5WBYBJxWwVByy3gmBWxJVvkyX/p0JgVf2pk71rotaUDlJhS03iqMtRR7zQ0DENk47CkKzzz77bKT30Z5jl112iT2saQdA+J8Norfeeut4OT9BV6UbTW222Wax/ymfS6uF1PIhLS83KyOUzS9vpZYbrDuqu/lctol8leSojE8K2bNVpNmbfjV3f09ShW72Z/aLUtg3aIHApf202mBcSlVEVxoPxph+wfnx5VGucr4ehPp8Nq1Tsthe+QxC2mzwC66q4MQBvWpbAuuR8Dh7E7tR1ZztgPXFdkXLIFoB0fqBFhct9beBfbUtT7hJkiRJUpbhrxoCN0D7/vvvYxUkd5LPPrhhUmr9QOUgQTD/coluuilW9hJjLi3nBmfffPNNyc864IAD4uXY3DCLwI8+jwRtlW4W1RoI4wjaCC2oruVScCoHqRRMrRiopqP6k16hhBksL5WGzHM5jAEVilSaMgYEjoRY9JqkgpRLuW+77bY4hvQ7zgdEzUXYy2XWVE0SlDHPpSo8CVgIP+mVSRjV0tXctN6gypWqS8aB8JOQrtLn1LIuqmG5GO/8De0SAjkqGOkzS6BPxSYVk1lUK3K5Op9PqEcf0RRQctMx1hutBrhhHPtMufAybQcEmOxThF5rr7124TX6BVPRShsOKozZVgjvCe5pK1AOVclUgdOaItunuSXGh/XDdjFo0KC4bxMU0zakEpaRCltOCDHtbNuCPPob03KC/YhtnxYz5dqLsC0zDqwL3k8P2Oeff76u8WD7Zkz5DMJLjjWsv2otTWrFSQJaM7Bd1IoWEKx3qsaplGUbY1tjOalcrhRw0mecSmO2F/ZvtkGWkX/L9Z5ujuZsB6wvQm3Gm99h3//yyy+rHhfzONnCtkRFM+udmxRy1Ulb/22QJEmSpCzDXzUEwl0CqVJVloS/BDH0P+3Tp08McW6//fbY15QbGWVvwnbUUUfFL/dU4ZW7XJ/Ai+CDy+XpV0moQSDFtOtBmMDd4JuLy/wJqrkEnOpXwjGCOvrMpr6QhJbchIz543JmbrZEcF2p8pegghCHSmbGgECWKkDaBTBWVMZRWcpnVQtV6kG4SLhEuMW6IYghNCsltToo1ft1VMcVjBdV04wXY7vTTjvFqt5RWReEwVzezfZVDm0VyoXMjDuhPScrOKlBi4l8X1dCWlo58PkE2FQcUiGdKhe5eRTV79wEjnVM0F4JoTbV84SE2fliOdgemA6hJZ/DjRSp1KbivBxOFNDrlvmkfUe9Ko0PCGep2qYFB0E4oWolhNtsy/QGZ1unF205nEhhvBg/rjLgJFK5an/CQ7YDpkvLFfoycwKlnvHgcwguCRGXWWaZWEFMUJtta1MOxyKmWw37Gy1CssfASsYee+wYbhLaso8svfTScVtjrKlaHjx4cOG9HGeyPcLpeU3Qznhz3GTfonqa3+G/W1K92wHHMSqtCeMZO3rwcpyvdlzMYxrcJI7thH2U9cXflFrWhSRJkiS1li5N2eu4JbUJKgwJsqiWqzc0VoiVdYT42RuadfRxJSzjBldcyp/v+Tk6IRT/+uuv4wmYzoIQkyr0fCDYUceD9imE1e0ZOlIdTXhda19n1WbEiBHxJGev/jeErt17OGySJKnNDB/U19GWOuB3gx9//LHqjaG94ZvUDrgcn6rjjhZQdnRUDlI5y6XUpar5OvK4UilL+Du6Br/8QaLtCDeP60zBb2cbD9oo8H8AmlNZ3ZI4AcN+KkmSJElqX1b+Suo0qGTkcnsqMAnNvIlS50EgT3sBWiKcdtppoTNpjcrfzjweaj9W/kqSpPZi5a/UeSt/DX8lSZI6AcNfSZLUXgx/pc4b/nrDN0mSJEmSJElqQPb8lSRJ6kSGHrlq1bP7kiRJkgQrfyVJkiRJkiSpARn+SpIkSZIkSVIDMvyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw19JkiRJkiRJakCGv5IkSZIkSZLUgAx/JUmSJEmSJKkBGf5KkiRJkiRJUgMy/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkiRJkiRJkhqQ4a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSZIkSZIkSQ3I8FeSJEmSJEmSGpDhryRJkiRJkiQ1oG7tPQOSJEmq3dwD7wtdu/dwyCSpQQ0f1Le9Z0GS1ECs/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkiRJkiRJkhqQ4a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8ltYjLLrssTDTRRB12NDv6/KnlHHHEEWH++ecfbYb0kUceCV26dAk//PBDi01zm222Ceuuu27ojPswYzF48OB2HT9JkiRJ6igMf6XR1NNPPx3GGGOM0Ldv37p/d4YZZginn3560XMbb7xxeOedd0Jb+eSTT8JYY40V5p577tCR1RtEtdY85B9LL710m85DSwZsffr0KSzH2GOPHeaaa65wzjnnhLaW5uGZZ54pev6PP/4IPXv2jK+x3K1tySWXDJ9//nmYcMIJQ0fS2uNTLuRnLFZfffVmT1eSJEmSGonhrzSauvjii8Oee+4ZHnvssfDZZ5+N8vTGGWecMPnkk4e2rALcaKONwogRI8Kzzz4bGt2ff/45Sr9/6aWXxlAsPW6//faS7/vrr79CZ7DjjjvG5XjjjTfidrD77ruHa6+9ts3no1evXnFss2699dYw3njjtdk8cBJkyimnjGFqR9Me48NYdO/ePXQ2nWXfkyRJktS5GP5Ko6Gff/45XH/99WHXXXeNlb8EqXl33HFHWGSRRWJl5aSTThrWW2+9QtXlhx9+GPbZZ59CZV+5S7LPPffcMPPMM8dwavbZZw9XXnll0ev87kUXXRSn3aNHjzDrrLOWDSWzmpqaYqC05ZZbhs022ywG2XnMz3TTTReny/S//fbbwmtUKPPZb731VtHvnHbaaXF+k6FDh8YKQoKqKaaYIn7eN998U3idsdhrr73CgQceGCaZZJIYOlGNmK2QBp/P56WfS11S379//zi97LT32GOP+Dzjv+qqq9Y0T+Wwbpi/9GB+hw8fHueLbWG55ZaL6/rqq6+O72e9zDnnnPG5OeaYY6TK2o8//jiGrkyXaa2zzjpxeqXw/PLLLx//e+KJJ46fyRikKlDGkBMHfBYVyc8//3zV5WG9shwzzTRTHPNK2w7TW3nlleM4Uh3Lsr744ouF17fbbruw5pprjhTEMU+ltq2srbfeOlx33XXht99+Kzx3ySWXxOfzDjrooDDbbLPFeWe+BwwYUBT4vfLKK3Gcxh9//DDBBBOEhRZaKLzwwgvxNfa5tdZaK47fuOOOG3r37h3uvvvukaqqORnCiZh77rlnpMCV6f766691r78rrrgiVuqyrrLYhtn+WmJ8SlWGv/zyy/G5UvPF/n3kkUfGMUvHoXQcy1bbp22ceaBCmm2MqwUeffTRsvPMsWLTTTcN00wzTVxX88wzT9GJhVrH47bbbgsLLrhg/EzWN/P7999/F15nvjhGrr322nGdHnvssRXHUpIkSZKaw/BXGg3dcMMNMdAjkN1iiy1iGEOgmtx1110xsFxjjTXCSy+9FB566KGw6KKLxtduueWWMO2004ajjjqqUEVaCmHT3nvvHfbbb78YWO68885h2223DUOGDCl6H4EIIdSrr74aP2/zzTcP3333XeF1AtNsoAqmQYi10korxfkn2Pnll18Kr1MJvP3228fwlACJQO2YY44pvE4At/DCCxeCzoSfCZNBCLXCCiuEBRZYIAZw9957b/jyyy/jvGZdfvnlMbjhM0888cQ4Lg888EB8LYWYqeq2llAzP22C8yeffDKcd955Nc0TAVi9FaAHH3xwXFdvvvlmDJkZh8MPPzyGUTx33HHHxaCS+QGBJe8jTHz88cfj/BFGr7baaiUrlKn+vPnmm+N/v/3223EszjjjjPgzwTmvMW0C2VlmmSVOO7sN1ILAs1x19E8//RTDxieeeCK2ICAoZlvjeeywww5xLLPb8p133hm3MdqZVBpXAlq20bR8H330UaymLxWKMl5Mh2pllv/CCy+MJxwStn32LbaT//3vf3G9jDnmmPE1KpsJG5n2a6+9Fk444YSS1bOExgTZ11xzTdHzrFPCScLMetffhhtuGP7555+icP2rr76KxwmC80rqGZ96sF44thCCp+NQWlelHHDAAfH9HM+WWGKJGKRnTwhl/f7773G+WT6OXTvttFOc3+eee67m8WBct9pqq7hfsb7PP//8uO7zAS/HNo61rNNSY8k6J9DPPiRJkiSpHoa/0miIakZCUxD4/Pjjj0WVcAQUm2yySQxmqf6cb775wiGHHBJfo0qQXsEER6mKtJSTTz45VnfutttuMWzdd999w/rrrx+fz+I9VNkR+hEyUpWcQhZQiUvFZn7+mT/mgyo+qupuvPHGwusEaywXwSKfTWVpqpzNBm3Zaj6qgQnceB5nn312DFmZJ4Jy/puQnOA529t43nnnDQMHDoyBImEPoTJhOSabbLKiqtv0c62YJoEyIT2PWuaJylbem8cYE/ClR7YPMdXFrJsZZ5wxTDXVVHF5TjnllMJz/EulNwEWqBT+999/Y3UwVZFsIwTcBHuleriynthuQDUtY8F8EthT+XjSSSfFamZ69xKIEuRWq7hNCOGuuuqqePKAYLwUnmd7Z8yY1wsuuCAGu2mbpyI0X5nO8hDypYC13LiC0I71AAI+guVS6/qwww6Ln0UYSvi4//77xxMxCePHCQ3mk3XP57PvpdeWWmqpON5s7wS8yy67bMn5YRtm/aYqXwJDgsm0bde7/lgfnBTJtm9gzKmsz1arl1Pr+NSDeWLddOvWrXAc4rlyOBHUr1+/uKxsc6zPctsYFb+sG/oJM9a0x+F4ktZVLePBsZPwnpMOTIPK86OPPrqwDyVMh5NivIffzzv++OPjvKYHJ1IkSZIkqR6Gv9JohspLwlXCQBCeUDGXDUKoll1xxRVH6XOoGCWsyuJnns8iPE2ooKVykSq6hCCV4Cah+pXq4xReg//Ozj+fsdhiixV9DtV+WYTHXBKebkZFZSSXaBO8gcvJCVWzgWl67f333y85/yA8zc7/qKD6MKuWeaKKMN/OAlSYsl7TgzAqIbBOCGSZFpXT2c+hcjp9BvPx3nvvxRMA6XXCXSoms2NTDe+lCjW7nVDpSpV5fjvJow0Fn0sQR/9fwmnamJRCdTTvIVAlQGMb4yQDYWdC9W8K83g/bROylZjlxjVtf9xAcdiwYTHcLFcNS+jKshJUMu+Ewdl54AQJ80EAPGjQoKKx5AQG64DfJ5wn7C6HcJVxTJWpVN2yzEy3ueuP8bv//vvDp59+Gn9mOTlxU0uVea3j05qy+z/HPLb5ctsYJxQIagnGGRfG57777itaV9XGgzHmKoDsPpT6VKdQPr/vlcJJN07OpQftOiRJkiSpHt1CM/Dlgy84XJ4KgiQuMaVqi8sjJXVchKT0nZx66qkLz9HygRskUVlKOFapgq6lpcvaE44tVCWWw7GGkCob7jL//A7Vr1T61oIAjopQprf44ovHf7PhIeEg1ZlcXp9HwNvc+UfXrl2L2myUu9kTYXhWrfNUbnmprs76+uuvR/ocPgNU4OYDdCp403sIpvNtMzCqFZ21oor10EMPjdsqy86YlkP1JZf4UxE+/fTTx22dMDDb4oCqbSo1CSmfeuqpWPG8zDLL1DQv9H+lEpfAnG2TKubUUiJhuswzFaFUobOf0a6ECutsCwAqQanSJXwm5OU9BM+EwvwerxE6UhHK71KVmkerkA022CBu05zk4F9O8BB6Nnf9UWVOFTL9bldZZZXw+uuvx3lpqfFJ6y+7X7TXDdCoRGdbOf3002MAzP5BdXx2e6k2Howx65qq+Tx6AJfbx/PYVjvjzeskSZIkdfLKX76cpr6dX3zxRawgIwDmiziVLpI6JkJfwgpCo2wVKFVqhMGpDQLVrKl1QSmES1THVcLl1fQSzeJnThKNanhN7878/BPUpUvL+Wx68GalCt8swjiqMVNVIkFZQhUwgQ6X6BOaZh/VApsswuH8WBGw5XslsxzVtNQ8VcJN5NgWGI/8ZxCIpvl49913YwuH/HsINcttM8iORboZYHY7IfCj52217YTP4fO4RL9S8AumT+UsFbH0iCVMy98kj4CSnrhU/1LFyaX49aCalZYJhMgpJM8iUCZ45u8k1Z5UIXMTtzxOXlDFTMBLcJhtLcAl/7vsskusfGcfIKAvh22bPsZsLw8//HCh5UNz1x8IoBkb5okq4npaEFQbnxQ6Z/eLavtELcehUvs/x0FavHCcKLe9cAM8KpYJeGnJkG31Ust4MMZcZZEfXx7VtldJkiRJaknN+gbCDVDSzZ/ogUfPTb7YUkWU7rYtqePhJlbff/99rMBjv80+6IeZWidQcUgQzL9cGp1uMJUQPnLTJi55zodo2RsscTygvyZB06mnnhpDK3ppNhdhEDcFI3TJzz9tLLhpGMEOQR/BF/2F+Wwqmvk5j3CNCkQqfrkpXLYamhtscdMxpksYyeXwXPpNKFhr4JTGiiCdE2WMPag45oZtBPHMH+PMcbWalpqnaqhYpLL0zDPPjKEX65+Ai3UIgkT6MBOQcWOrDz74IAZ7jPsnn3xScpoEn1RFsw1ScUxlJIE1Y8+2wvrhxlhcGs9l8WyjLYWglX6+bMucFGD+S1W3s12xDfE+qoXrQU9YlqvcCVDmgbYBVPKy3hhbboqY/Pbbb7G9CeNIKEwAyTpOASWVp6xrxpp9gBOw5cJL0A+Yam+WldA+W8XdnPWXTvzyOqFzva0bqo0PoSjhKdXP7BNU0WarosvtW8w7xwWOQ9wcrZz//ve/cbxp3cF+xL5YbhlYV9y0kf9fw7bAzSppBVLPeHDDRPZv9iUCeKbDuqfVhyRJkiR1+PCXyqx0GeKDDz4Y1l577fjf9J7MV7NJ6jgId6lQK1XdR/hLIEkvUW5axA3U6BnKTY8IK7M3YSPAoV8ulZvlLhOnipJLpwlgqbbkRkcEiLXcICof8BAIpfmnIjT1uc3i0nh67d59992xjQOBDJ9P5R5VlKVCF3qe0kaByuFsZSQIggngCFW5rJvLvwnguHlbPZV7BFgESQRbXCoOLt8fMGBAvCHdIossEgNoKiKrqWWeCNxr6cNaCSEoNwNjffEZyy23XJxuqvzt0aNHDP+5QRUBOiFkuqSf3rKlUKGbboJFdXHq40xvW7a9LbfcMlZL0ouWkHPiiScOLYXthrCP6fM5hJxUveaxb9BCgvWTPRFQy7jyGoFqqnDO4+8kFb0sN/sUwSLbQEI1LK0p2A6o/t1oo41iewTGDKxzQkvGmiCV99D3uNL8cJKg1LbdnPUHjhusK/rXsn/Xo9r4UCHPCSfCWa484GQTPY4rYV4YC07ccBzK3sAxj+2MB8eDJ554Ih7b8jeSTDhWsK2wHXC8IkQvtbyVxoPf5UQHxx72cY5J9N3mJIgkSZIktaUuTfnGkzWggogvW3379o0BBJdT8oWKf+kzWKlySJJqRQUol+PT/7Te0Hh0RRXxo48+Gis5VR+qkQmpCb3zvVod1/+PG0FyMofK5c6Ak1SctHjppZdi6N7Zx2PEiBExdO7V/4bQtXuPNvlMSVLbGz6or8MuSarpuwE3hq5UxNPsG75RkUOVHTdF4dJYgl9QSZPaQUjSqOLSdqqODX5rR1BOmwvVjhv00TaAKm2qqNPVLI7r/6FymhMKPCpVHI8uHA9JkiRJnUWzwl+CGL4okzJnL83daaed4uWkktQSuLqAh2qXbc+h2tCLl+rQaaedNrZ36NZt5D+No/u40rKEwJOTv7PPPnsY3TkekiRJkho6/AXdIrhbNjeu4aYn9M6kl5/hrySpM6GvdDM6II1WaJ/QGbXWuu2s4yFJkiRp9NOs8Jc7kXOTFaqluLv2yiuvHMNfKoL4+bzzzmv5OZUkSZIkSZIktW74u/fee4eFF1443kWcmzEl9AHecccdmzNJSZIk1WDokatWvamDJEmSJDU7/H388cfDU089Fds85C+v/PTTTx1ZSZIkSZIkSWpnXZt7Z/R//vlnpOc/+eST2P5BkiRJkiRJktQJw99VVlklnH766YWfu3TpEn7++ecwcODAsMYaa7Tk/EmSJEmSJEmSmqFLUzNug/3xxx/HG77xq++++27s/8u/k046aXjsscfC5JNP3px5kSRJUhkjRowIE044Yfjxxx/t+StJkiSNxkbU8d2gWeEv/v7773D99dfHm75R9bvggguGzTffPIwzzjjNnW9JkiSVYfgrSZIkqdXD37/++ivMMccc4c477wxzzjlnPb8qSZKkZjL8lSRJklTvd4O6e/6OOeaY4ffff6/31yRJkiRJkiRJHf2Gb7vvvns44YQTYusHSZIkSZIkSVLH0605v/T888+Hhx56KNx///1hnnnmCeOOO27R67fccktLzZ8kSZIkSZIkqa3C34kmmij069evOb8qSZIkSZIkSeqo4e+ll17a8nMiSZIkSZIkSWrfnr+SJEmSJEmSpAYMf7/88suw5ZZbhqmnnjp069YtjDHGGEUPSZIkSZIkSVInbPuwzTbbhI8++igMGDAgTDXVVKFLly4tP2eSJEmSJEmSpLYNf5944onw+OOPh/nnn7/5nyxJkiRJkiRJ6lhtH3r16hWamppafm4kSZIkSZIkSe0X/p5++unh4IMPDsOHD2+ZuZAkSZIkSZIktX/bh4033jj8+uuvYeaZZw49evQIY445ZtHr3333XUvNnyRJkiRJkiSprcJfKn8lSZIkSZIkSQ0W/m699dYtPyeSJEmSJEmSpPbt+Yv3338/HHbYYWHTTTcNX331VXzunnvuCa+//nrLzZ0kSZIkSZIkqe3C30cffTTMM8884dlnnw233HJL+Pnnn+Pzr7zyShg4cGDz5kSSJEmSJEmS1L5tHw4++OBwzDHHhH333TeMP/74hedXWGGFcPbZZ7fc3EmSJKnI3APvC12793BUpBoNH9TXsZIkSaOtZlX+vvbaa2G99dYb6fnJJ588fPPNNy0xX5IkSZIkSZKktg5/J5poovD555+P9PxLL70UpplmmlGZH0mSJEmSJElSe4W/m2yySTjooIPCF198Ebp06RL+/fff8OSTT4b9998/bLXVVi0xX5IkSZIkSZKktg5/jzvuuDDHHHOEXr16xZu9zTXXXGGZZZYJSy65ZDjssMNGZX4kSZIkSZIkSe11w7exxhorXHjhheHwww+P/X8JgBdYYIEw66yztsQ8SZIkSZIkSZLaI/zdd999R3rumWeeiS0gxh577DDLLLOEddZZJ0wyySSjOn+SJEmSJEmSpLZq+8CN3S6++OJwwQUXhEcffTQ+qATmuYceeiiGwwTAb7zxRuisttlmm7DuuusWfu7Tp0/o379/u8zLI488EoP1H374oebfmWGGGcLpp5/eqvOl/++yyy6LN0HsqDr6/HV0+fE74ogjwvzzzx9GB2217TTnGNea01H7Yh0OHjzY1SBJkiRJ7RX+UtW70korhc8++yz873//i49PPvkkrLzyymHTTTcNn376aVh22WXDPvvsE1oznOUL4qBBg4qe5wsjz7e0W265JRx99NEtGq6ecsopYeKJJw6///77SK/9+uuvYYIJJghnnnlm7KX8+eefhwknnDC0p3oC8BEjRoQBAwaE3r17h3HGGSf07NkzLLLIIuHEE08M33//fWhPTz/9dBhjjDFC37596/7dUut94403Du+8805oK+xrtF6Ze+65Q0fWEQKc1pgHbmzJSa5RDVWZNx5du3YNU001VdyOPvroo5H2ufQ+rqqgv/o555xT9J4///wznHTSSWHBBRcM4447bjxOzDfffLH/OsfoSjhxt8IKK8SrNHr06BFb92y99dZxmu2xbY/q8aitjpX//PNP/NtD73uOb4zfYostFi666KKK8zcqOtoJvbRd5h/XXXdd6Ch+++23uG4mnXTS8Mcff7T37EiSJElS5wl/CRoIQgknE75sUxFHuEeIQD9gQuHWRBhywgkntEmYyBfI8ccfv0WnueWWW4ZffvklBst5N910Uwxgtthiixj0TTnllK0SareG7777Liy++OLh0ksvjUHZs88+G1588cVw7LHHxqrxa665ZpRCl3///XeU5o8K9T333DM89thjVcOxWhD+TD755KGtEBxutNFGMWBnbBtdCiI7ivHGGy+ezBhVHD8JKjlZdvPNN4e33347bLjhhiO9b8cdd4zv40oK1vvuu+8err322vgagRYn3bgJJyfE2Kbpw85Jo2+++SacddZZZT+f6a222mph4YUXLvwe7+d4w37WHtv2qGqrY+WRRx4ZTjvttPh3kHEcMmRI2GmnnequOG5qagp///136Kw4xrNtZh/ZK2bae19nv+IEJCF9e5+IkiRJkqROFf7++OOP4auvvhrp+a+//joGUuBS4dYObag+5ov+8ccfX/Y9pS7RpnqKKqqEoINWFcwzoc6BBx4Yv5RnZau4+O8PP/wwVjanaidCXMIcQtssvnBSjffTTz+NNG+EKmuttVa45JJLRnqN5/gSTehc6lLm9KW2e/fucVmoIq6E391hhx3CZJNNFueTar9XXnllpHG68sor4/QI8zfZZJPCfBMsUSV4xhlnFJZ5+PDhJT/rP//5T6xgfO6558K2224b5p133jD99NOHVVZZJYZWu+22W+G9BPdbbbVVrIDmpMHqq68e3n333ZEuO7/99ttj1SPLy7QJvQiWp5lmmji+VN0xTtVwc8Lrr78+7LrrrrHyl+nn3XHHHbFKmZMLVIytt956Zdd7dh6zzj333DDzzDPHMGr22WeP45rF71IlyLRTxSXLWA3bJYELJw4222yzGGTnMT/TTTddnC7T//bbbwuvUcXJZ7/11ltFv0OQxfwmQ4cOjeuCoHOKKaaIn0eYmDAWe+21V9xX2EbZD9mGkrR/8fl8Xvo5304F7FdMLzvtPfbYIz7P+K+66qo1zVM1bK/MCydbll9++Tg+VMhSCV7r+JU7prAu55xzzrjNEDTlq3NLYV4YN6p+qVjdfvvt4z6TjqEJ88H7ZppppvjZ2W2F9fbEE0+Ehx9+OK6PhRZaKM77csstF84777wYCpdz//33x+lywo4qctY/YTAtfAh9K7W84PjE57Au2J85hjIdpsdxjRM9+XF/+eWXi45HPFdun2XMuYqE/Zvln2eeeQqBd6XjUXOOlTzHOG233XbxBB/LRUujShh/lpuwfsYZZ4zbEeuPY1It83fPPffEdcU8sf7ef//9eEUN2zVjyvHnwQcfLHxeuWMP+P1lllkmrrNevXrF7YC/RwmBLMc6XmdeOfmWrSJmuddcc82i5fvrr7/ieix1fMli22CdZx/sA7Wsw0r7ehZ/q3hP/v9rcGytVoHP/HMClUepZXn99dfjsvM3kXXPOLIuErbztO2wn2bno9rfVP6b4wzT5XXW9wsvvBBfY13yt5+/e/z94jPuvvvuissiSZIkSW3e9oEvjLfeemu8BJ0H/82X3xTsEGLMNttsoTVx6T5f2qlWYx6aizCAkIMvenyRpnKV5SmH8GjaaacNRx11VKHaiS9whKUEc1n8vMEGGxSqhvmySzCQMGYEN3wZTIYNGxYr8XitFCqqqQDk86jWI5ChxUKpIDMhpCCwJ3Tg97lEfMUVV4zLmvCll7D6zjvvjA/Ci9RWgxBjiSWWKFQh8iBoAEFCCv6oyiVc5cv21FNPXXJessEFY8EXYsIUQjjCzTXWWCOGD9kWGFR4E7DxZZ1Qgi/hvJ9LjF999dW4fARX2eCYz8mPyQ033BDDOQJZ5pF1ng3677rrrhj4MQ9UKRMuLLroomXXeylsO3vvvXfYb7/9YmC58847xxCc6sB89SDrkfnn8zbffPOi9ZEd14RpMB6c+GD+Wf5s0EMlMNsN40PYRvhwzDHHFF5nn6TS8+qrry6aLj8TJqdQgyBjgQUWiOvm3nvvDV9++WWc16zLL788bvd8JsEf4/LAAw/E155//vmiysD0c62YNuHOk08+GUPMWuYptVKo5tBDD40hHePDeBBQperLauNXCmPHlQ4Enm+++WY8JrE/sgxJfr/PY99ku+GYxqMSQrx0Yo0wjcpfxqWU7Hjkx4egjnXDsaYeHCc4jrAO+HxCNcJFjsEcM9hXaTkxKlXptMIhLGN/ZB+iqpawn78r1Y5HzTlW8jeA/YJ9nlCXk0NUYpdbf4wdx21CyFKqzd/BBx8cj61sL5wc46QUxwCON8wDxzLCwdQGpNyxh3XBe/v16xePIxx7+RuWDSk5ucYVDgTPBOEE29mTtwSYrMvs8YzjP8cZ2n7Us2/Vsw7L7et5zB+BdbZtw1VXXRVDZY4J5TA2/I1g/fN4/PHHi/7OpvZUBLusS7YV/n9NOhZwAo8qe+abbYe/UdzLoNa/qRzPWWcc+3iddT7mmGPG15guy5Mq7tlnCP1L4X2cEMo+JEmSJKke3UIznH/++bECiS/U6YtSt27dYq9IKtFAwJbtf9haCOqoRBs4cGDVKqVyqIA65JBDwvrrrx9/5gvofffdV/b9VDoS0BDoEgJkv6SmnpNUCfHFkGqebAUXVWW8llDpREhKSJaCPr5oExTwRbKUU089Nb5GiAECLC49ph1HqYCJMIAv3MwPX3Rx8sknx6CXSmW+3Kbgls9OQTVf1AkjCLWoBOYLeqpCzKJikKotEIYQ1BGuZhECpDCFUIPQiKCWL9R86WfcUpDGsjNv6RJ4gmAqKamuA4EI48W/KWAmzCPA4PlU7cg85Ht/pkowEJpQxU5glSpPWVa2a4LZJH1uufWex9iyHlKFM1XlzzzzTHyeMDHhPQSPYJ65VJ/1xHzlxzU7/8wf80G1JtWgN954Y2G9Ezrx+1Tkpm3jqaeeimOTEEqcffbZhR7WVAMTThCogNcIE7NVo4TkrBfem07qEFqx34FqVH6P7YUwkmq4bGVgvZgegXJCAFttnljX+e2uFLaV1O+Z9UzV3XvvvRePWbWMXx5jQHiYjh9UV7I/cpzkmFhqvwfbHoEPJx8I2kDVJoF6KVTXst8Q8qV9lmXPVk2nY2IK4VlHzD/y48P+xXGOKmHWEa1aOK4QFmZb+uRxnGDs2Q+oxmebZt/mWEf/Yj6DMIsTFVTkNwfBXqqiBW1amFdO3nAyptLxqDnHSoLXtL8edNBB8e8Y85/GK7/+mC4n9fhsth+OX5wUpTI9jXWl+SPEZT9JOLak4wzYNzkZwPGRILfcsYerXtif01Up7DccR1inhJdUG/P3hwCScBv8XeZ9CfOerk5I2z3HUbaPFEiW27c4fuVPVjC+jFe1dVhuX89jv2IMbrvttsLJHv5Opb7/5bCNsj6ork1/a7N/Z//73//G5eIEWgplsyesOeZwAo8TeQkV2bX+TeXv0wEHHBCPK2k5E14jsKcaGhzHy2EdZ/8eSZIkSVKbVP7yhZBLg7mskyolHvw3FUUpuCCQzV8a3VoIGqgeooqqXgQwhLXZkIIgO31RrgdfaAkCUsUfYRrtDqguSq644oqiNhV8cSYg4sssIRDBCr9PpShBSiks51JLLVX0HD8TpqZenVlcfkplGS0tWHfp8cEHHxRd4kqlabavcQqwqyHwy1+Wm0eQQSUlX8C5CU9aDsY6O/bMIyFDdl0SohBiJVRKsZx8Uc8uDyFudnlobZBaNoCAii/sKXDls6lsy540YB7Lhe61Krd+8ttndpnYbwjcsuOdH1dCdSoAU3iN/OXMfEY+cKMCMYvwmFCIQDoF7lStpZCC7YXgKzu26bXs+Gbnv57tpRacLMiqZZ5Y1/l2FqVk5zsFemm+axm/LKqu+XyqhbPzRnCUHav8fg/2NbY3KpkJj1kH2XYJCSc+mCYVv1SScuKNytRyeD/TpYoxhcqlxodjD2EYFbuEb4R1hOscw8pVtZc6TtCqgBA4e7ziuVHZFti/CUAJxwg+WX6Cw/wN8aqp9ViZ3SZSO47s/OfXH8tLNSv7EOPMezmpxQnAWuT/vnB8JiildQgnTFhe5r3a8rJf8Lcju+1xjOXvCMd3jnkc59i2EqpXUyCaMN/pqhUq6qlmZbmScvsWITnbWvaRTsjVug7z+3oebSQ4EZnaI9E/nrGvVEnPZ/N3NH+sZKxSz3jmlTYPKfjNYn1SLV3ub0Etf1M56ce4cpUGVd7Z4wEneThGsC1y8ogTOuVwYpr/n5IeH3/8ccXxkiRJkqQWqfxN+LKTD4DaA+EqX3j5kpT/Qkggke/fm20p0NL4skdFEZd48mWaELfa5bJ8ySZY4NJTvpjy5Y7fayl8SSXkKtVfM9vPM/8lmPmu9+ZqVHwyzewl06ASDIRG9d4UidArO4YsD8EV1ar5qrNyl86CkJRK9Ww7CrYNKreoWqUKLPU6bQv1jjeXPnMpdTacTCcMshW51RBscbk006Pak3+zYSLjS5DFSZW8bPVjc7aXWvfHfPVrrfNUi+x8p+2quTcRZL7AybB8aFytfQNjkS4jJ/QjHGI95PtDU9lJqwq2TZY1G7JSTZjf19J4ELjVgtCXcI0HYR3bEVc/lKs2LLXeK20LaX6z673aMZjKXKqwuSqD8JDtgerW1uoj39xtmUpQHswbJ/sYQ9YV1d+V5Ldvgl+qtakeZZtgXVNZXG152f5oK0OYmMcxl+NCLaj25m8WbRKoFGf+CUZrOZZkWyE0Zx2Wq3TP/13lZDInKvi7yvGLE6vlEDLT1iG1rciGwunqhErH+mp/B2r5m0qFMa10aHtBmE7IS5UxQTrLw/9n4TV6b/P3nxNAVEfn8fcpVRdLkiRJUptV/nZEVNZwo678zZsII7/44oui4CF74yECP77EZftTEhASLFZCNWqpKluqi+gryKW3XP6aLvuuhMv7uUyXyia+2FIpVOmLLUERrRKy+JnQplTgRNUXY0AFGF/Us498W4HmLHM+EOHSXIIQKqcqYTkY6+zYU0FOmEVlXTlc/s98UJ2VX55yl4DzOVTv8QU7W6VGBRdhcLoRESczKt1EqJYxKLd+Ki1TLQivuQw5P/+ENKkqjs/O91pNFb75QJH+oOwv9JimGji7vdBbmQrP/PjWEtRkA7X8WLE/5qtKs/tjOS01T9XUOn7ZCle2H8YwP1/VAsA8wjfWCZWNWRyjmB4hbf5qAKrYCQ25+qIlUBHK8TDbR3pUpRYg2fVebZ2zv9BGgeMp7RC4LD4fZI7KvljuWDkq0v6dxq6W+cvOEycuCQYJSjmO5W+oWWp67Bf8nclvezzSzSY59mW3D1qccKPNLCpY6dfP3x+qY1vi5GMt67BWjAnV0pxk4WRVtiq5lNQeJ1+VzHPpSgmO9fQBLnUigpOUHGvK/S2o9W8q2xmV+gS8tK/I3hOAljW77LJLvJqD4zrLJkmSJEmtoWHCX74cEmgRumbRD5M+tFzWTGUdVblU4WTR04/wmH59XNpK78dq1al8MeRmLVQXffPNN0XhCV/y6PW3yiqrxBu+1ILLxvkSmG6cVwlfFPlSSpUeX6a5vJXK1Wx/xSzCZC5d58s9X0IJFajuokIt3X28FiwzwRi/zzKXq4zj0nGCKtpgEEpySStjz7IRNqbQhapFwgEuZaeHIkEmQQG/y/Pl8IWadU21GmPGpba0c6B6ikqqUriBEYEHY0uv3OyD3ospEKA6iyCYf7nsOt2Mp9p6z2LdE6DQc5PLy+kPynyWWz+1ILggFKRiLD//BIBsA4Q8VADSn5YKQj6b7aJUv1q20Z9++ilWmtKzNVsNzc2IuGkR06VXKOuOSjoCoVrDLKTwhJAkhU1U7LHNEcQzf4wzl3BX01LzVE2t45dFhSzbHsce9ke2GUIe1ns9CIMI/7h5XK0Ilti3uTydKku2EfYHxobjXKWAk57ErH+OCYwn4Tr9bvmXKuuWQhUlFebpBme0Z+GGcJVwbCDU5jjF71DdSjuCeo9H9R4ra0VVLi0P+HxO9lEByjbKsSm1I6n1eJmWl2NEOqFDxWj+/aWOPawvxijdoJBtlt64qV0M88Lxnx60HCMJgfnv/NUU4NiS2ifVctIS/J1k/84+UvhdyzqsB/PHNsSJ3Gw7nzz+3nMimGXIHyv5m8HfeY4ljBE3TyMQ5pjE2FF1nyrpqdzlZCH7Na+xb3Fz2Vr+ptLaiOmzXbB9EIRz3OJkBKiAZh9lX2W6tLRJr0mSJElSS2uY8DfdRCf/hZkvVPTAJPSl+ogvwPkv/gQEXK7Ll0W+0FH1U+nLZfosvvBRtZsq2xICRi5tLVWdlL9rfEIAyaWd3CCIL5SVUHXETXO4hJQvtIRFzE+5Hoh8yedmTLTHICwjoOALL19KqVysFeNGmESFG8ucejcSSqSb6KQqMsaZL9pc+ksITDjPe7gMN1vhREhGz8c111wzjj1f7JnXUn0Ys/g9ps+6o7qNMePLdWovkZabEBaEu3xhz98ALo09X9gJqVk/3ECNGy1xmTFhZfbu9JXWe8K8EMQRINI/lZCN+c3fmKua7Lgy/4x7Cpay2FbTzQUJ2RhfPp/tnWCiVNDGNk7AR9BEkJ5FEExYQajKCQzWHWEFlzOX60NdCsEJ4Q+hJtXa4FJnbr7FjaW4XJ4AmvVYTS3zxLqu1mKlmlrHLx9KcRMt1jHzRRU/85Kt/C2335cKczmBkd3mqvVDJdwkBOTzl1566XjMY2zoJ0rQleTHh/2Sy9epPmQ7Zb6pcuZ3+O+WxEkgTk6wrzNv9DuthDHnOMf2wthRCZs/LpY7Ho3KsbKc/PpjvggY2Yc4nvK3g32T7YVq0FrnL+FEAScOufka02T62T695Y49VK8SphNscwUA+xnLmD2Zw4kWjvMc/zlWcLKN/Z9tJ4vjI1Xf6SakWeX2Lf6e8DvZRwpIa1mH9eDED2PLv/l5z2J5uRqgVL9eniP45soU/k7Raol9gO2dbZN9P/3tYZ3SsoL//8D+wd8oQuBa/qay3rmKhWMbr3E1DDefS61UOI5xsoB9lRtM8h4+R5IkSZJaQ5emfANOjTKqhwhxaHvApbdZtHPgC2C94UNHxQ2l+BJNlWG94WZroqKKL9RcEp29y3pn0VHHtSOjipggrFQfzvbWEfb7jjw+HV1HWH8thb65nJB58MEHiwJSQlCuuuAkAlcGdLRtJwXfnOTLB+OjE6qVOYnZq/8NoWv3Hu09O1KnMXxQ3/aeBUmSpFb5bsCNoSeYYILWu+GbRg7s6GvJpalc4poPfrmcmhVTS6VjZ8HlqlTHdrSAkqosLm/ujMFvRx7XjoygnEv6O5qOst931PHp6DrK+muuVN1KVTp/n6i656oCqlbB1TK0kaBSn0r6tddeu0NtO/TkpYqWSmIq80fn4FeSJEmSmsPK3xbEJfrHHnts/FJN38XxxhuvJScvSVJd6C1LexxuSki7B1pL0M4g3VSUilpalNCfnvYOpdoltCeqjelLzpUcN910UwyxR2dW/krNY+WvJEkanSt/DX8lSZI6AcNfqXkMfyVJ0ugc/jbUDd8kSZIkSZIkSf+fPX8lSZI6kaFHrlr17L4kSZIkwcpfSZIkSZIkSWpAhr+SJEmSJEmS1IAMfyVJkiRJkiSpARn+SpIkSZIkSVIDMvyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw19JkiRJkiRJakCGv5IkSZIkSZLUgAx/JUmSJEmSJKkBGf5KkiRJkiRJUgMy/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkiRJkiRJkhqQ4a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSZIkSZIkSQ2oW3vPgCRJkmo398D7QtfuPRwyqYThg/o6LpIkSRlW/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSZIkSZIkSQ3I8FeSJEmSJEmSGpDhryRJkiRJkiQ1IMNfSZIkSZIkSWpAhr+S2tVll10WJppoog67Fjr6/HV0+fE74ogjwvzzzx8axTbbbBPWXXfdFp1mly5dwuDBg0NH0qdPn9C/f/8W31daY/wkSZIkSf/H8FdSXZ5++ukwxhhjhL59+9Y9cjPMMEM4/fTTi57beOONwzvvvNNma+GTTz4JY401Vph77rlDR9YRAsDWmIf9998/PPTQQ6M0DUJG5m3OOecc6bUbb7wxvsa21hbOOOOMOD8dSVuMT0fYl9sCy8l48ejRo0eYZ555wkUXXVT0nkceeaTwHh5TTDFF6NevXxg2bFjR+1566aU4RlNNNVXo3r17mH766cOaa64Z7rjjjtDU1NTGSyZJkiRpdGH4K6kuF198cdhzzz3DY489Fj777LNRHr1xxhknTD755G0ajG200UZhxIgR4dlnnw2N7s8//wwdyXjjjRd69uw5ytMZd9xxw1dffRVPRuS3z+mmmy60lQknnLBDVoa3x/i09b7cUv7555/w77//ln39qKOOCp9//nkYOnRo2GKLLcKOO+4Y7rnnnpHe9/bbb8djIgH766+/HtZaa604bdx2221h8cUXDz///HO4/PLLw5tvvhnuvffesN5664XDDjss/Pjjj626jJIkSZJGX4a/kmpGcHH99deHXXfdNVb+lqp4pIptkUUWCWOPPXaYdNJJY7iRLhv/8MMPwz777FOokCt3qfi5554bZp555lihO/vss4crr7yy6HV+l+o7pk013qyzzhpuv/32qvNPdd2ll14attxyy7DZZpvFICyP+SEcY7pM/9tvvy28RlUjn/3WW28V/c5pp50W5zchJFp99dVj0EkVIJ/3zTffFF5nLPbaa69w4IEHhkkmmSRMOeWUsR1Ckqoy+fxslWapS+S5FJ/pZae9xx57xOcZ/1VXXbWmeapm+PDhcV5uueWWsPzyy8fxmW+++UYKFyuNX7m2D6xLqlTZZuaYY45wzjnnVJ2fbt26xXV4ySWXFFV1U4XJ81nvv/9+WGeddeJys/xsnw8++GDRe/hMtiPmgfdtsMEGhdduuummWPFJuElwvdJKK4VffvllpHVywQUXhKmnnnqkIJHP3m677Qo/EwQuuOCC8bNmmmmmcOSRR4a///675HKusMIKcX1mff3113HfqFRBXc/41LJdZdW6L6d1ff7554devXrFbYITL5WCTgLRpZdeOk6HsaYylvVXz3j88ccfscJ8mmmmiSH4YostFpc7SfPJMWOuueaKVbgfffRR2Xkaf/zx4z7KujrooIPiPvvAAw+M9D6Cb6p6l1122XD44YeHN954I7z33ntxW9l+++3jMfOuu+4Kq6yySpwW2zzPv/LKK/EkgiRJkiS1BsNfSTW74YYbYjhHIEsFHMFS9nJlgg0CvzXWWCNe4kwYs+iii8bXCA2nnXbaQhUdj1JuvfXWsPfee4f99tsvBpY777xz2HbbbcOQIUOK3kdgRpD06quvxs/bfPPNw3fffVd4ncA0G6iCafz6668xvGP+r7vuukKIByqBCWMIl15++eUYch5zzDGF12ebbbaw8MILh6uvvrpouvycArUffvghBlQLLLBAeOGFF2KY9eWXX8Z5zaL6j2CKzzzxxBPjuKRA6fnnn4//ElQzTunnWjFtwrAnn3wynHfeeTXNU2oVUM2hhx4agzXGh/HYdNNNC8FltfErhbEjKDv22GNjNeRxxx0XBgwYEJchGzYSUOYRqLJNsk7TMqy22moxvM2ftGAbYXtku+Q9VGWmwI8xIYxnHVC9yfgQ4IHxZxn5LOaPEHH99dcveZn+hhtuGMPu7LbKNsn02D7x+OOPh6222ipu44SDBKPMN8tfyg477BCuueaaGGgmV111VQw2WaeV1Do+9ap1XwbhJ/PASSHGgfHfbbfdyr6f/XHfffeN64T11bVr13hMSYF6LePB9sdJCfZvjg+sF5b73XffLfwOY3LCCSfEEw9U6RLcpvYNnOgohXm4+eabw/fffx/3r0o4UZAq7++///64XXCyp5xy+x7LyVUK2YckSZIk1cPwV1LNqJQlNAVhChV8jz76aOF1AqxNNtkkBrNUtVEZesghh8TXqJajV3CqouNRysknnxyDPgIiwkWCIMI2ns/iPYRys8wySwwMCfiee+65wutU4lL5mp9/5o/5oOcv1Xdcop3t38pyEdLw2QSCqXI2IcS79tpri6qB//e//xXCvbPPPjuGrMwTQTn/TUhOIJjthzrvvPOGgQMHxmpTwkBC5VS5ONlkk8V/qU5knNLPtWKaBMqE9DxqmScqD3lvNQS/VDAyPqxnKkAJ+GodvzzG4JRTTonreMYZZ4z/UlFKKJpQSUxFZR7LwTqkMpcwlnAzW2GbsB1yEoF1ztgcffTRcftI1eKEwATxVJnSh5XpMu8g2CTcZr44oUAFMNsmFcR5E088cayuJpxMmDe2Q4JwMGYHH3xw2HrrreO8r7zyynF+ssubxeemauGE5WT7rxbW1zo+9ap1X8bvv/8errjiilgBTKB+1llnxVD2iy++KPl+euWyzOzX/A7b6WuvvRaD8lrGg3XJSRP262WWWSauZ7ZZqol5Pvnrr79itfeSSy4Zt3uqknnw32OOOWbRPFHty/qmQpiKcNYzIXQ5bDMcrwikmV7ax7L7Fyd0mGZ63HnnnSWndfzxx8d9Mz2ooJYkSZKkehj+SqoJFZGEqwSu6bJybl6UbZ1AteeKK644SiNKdeVSSy1V9Bw/83wW4WlCcDfBBBPEHqcJQWr28nCqX6lYTOE1+O/s/PMZXCKetcQSSxT9THhMZeAzzzxTqFzlEn5CVXAJN6FqNthJr2UvX8/OPwg3s/M/KhZaaKGin2uZJ6or8+0sSsnOdwpk03zXMn75Kk8+n2rh7LxRLZwdK8JDQrBSCDMJ9TgJwfSo8M3jxAABICckCNT5DOY1Vf4SwBL6EpTSDoN1mqplCY7Zpgl9qSC98MILY+VnOZwEoDo0VaYyLbYZKljTuqBiNru89JAlMEyfmUVrCOYptW948cUXY0V8qUro5o5PayK4JwTNbg9U0HI8KYXqXI4xrAv26dTyJK2rauNBUEyfXU4+ZMeY5c9uU1Tu5vdBrlJgH8jOLw444IB4bHv44Yfj9k2bF8LpPKqhORbR+oOxZjsoVyHMZzNNHry3XNsPTp5xki09Pv744zIjLUmSJEmldSvzvCQVISQloCDYSKgmpBqOylKq0tKlzm0hX51H1V+lmzZRjUkVYjacZP75HSrzCItqQZUjl5czPW7gxL/0QM4GjbQU4JLyvGz1ar3zDwLEfLsBKhjzCKCyap2nWmTnO1WeVpvvcpgvEKjmQ2MqS2tB2EqlMS0+CAU5KZFH8EtLDaoxCe3YTqngTDfDo4KVEJHL/rlEnzYUTI/qTMJifvepp56Kr1G5SusLWlxQqZzHOLOOaIFCb2HaPBAWZpeZ6t9UwZpFsFkKVaZUwdKzlyCX7Y+wuqXGp9btqi0wfiwb20Tqn0zFdvbGhZXGg/Fl26EaP78NZau12QZqaXMCKrfZbnhQUcyJACr16RecxbomsKaFBNtUQrU5CLw5ZoDjZqkAOY/38ZAkSZKk5rLyV1JVhL5UX3J5fqpW40EVIwFNaoNANVulm1BRBUdVXiVUZ9KrNouf80FLc8Jr+gjn559Lw1MVIZ9NqJeVKnzzgRo3vqOv6LBhw2JlZ0IVMD1EqVhMgVF65EPZaiFrfqxo/5Dvr8pyVNNS81RNreOX0HuW7YcxzM9XqWC1XAuCtddeO1Z2lmtpwPZDZSjVzQR3BPj5vq6EovSCpl0GfWJ5nUpPEBJSfU5oS89atmN6U5dCgEuwS8Uv+wWX+jP+Cf9NCJhfXh6pOjgvhY0EopxsqKd1Qy3j05ztqpZ9OVXsfvbZZ0XbA8tZqsUIfXEZm8MOOyxWW7M9laqyrjQetLpgvqhGz49vpfYUtaLtAlc8pHY2WWyztJnIBr/gBm+sh1InXyRJkiSptRn+SqqKfpSEMFyeTxVe9kGPztQ6gf6tBF78y2X1XIKdDTwIHx977LHw6aefhm+++abkZ3GJNT08zz333HgJ+KmnnhrbNVC92VwEWVR2UjGYn38uMefmYgTc9HnlplRUiPLZVDTzcx7h3k8//RQrfunlmq2G3n333eNNvpgulaNcan7ffffFm9bVEpZlx4ognd6oKQCjwpEbYRHEM3+MM5e8V9NS81RNreOXRaBKS4czzzwzVmCzzVDNyXqvFdsL21NqZZFH5SXbUAr8uTlftlqZ7ZvP53V6GDO+vE5ASZhNr2TGnSCT6Xz99dcxmCyHkwNU/nJSIfWCTqgqZvosN4E8+wk9cAk8K2HbHTRoUKzQJcSuR7Xxac52Vcu+nMJw+hsz7lTGso1wo8FSQSy9dHv27BkuuOCC2Eea8J2e3/WMBxX8jDl9tFlXH3zwQWxXwzbGOmkJ3KyPG9gxZrWg4pgby/H59Mtm3+OEBycZONlQT6W7JEmSJNXL8FdSVYS7VEXS2iGP8JcQhCCjT58+8bJobqTFZdmEStmbsNHrlIpKquPK3cRs3XXXjTcOI0Ds3bt3vBEWYSDTrgfhFJe6p/mncrhU+EVwRJXg3XffHS/JppqQz6fXK5f5lwrlqOzj8nQCrXy4RxBMpSmhKhV/VCn2798/tg8oV9lZClXWtBug0pBqRnDztAEDBsTL+GkpQABNyFVNLfNEQFjrZfDl1Dp++RCPYIx1zHwtt9xycV6ylb+s+0o9brmEn9CwHIJkgkVu7sV6Yxyz1biMA0Eh2yuh7nnnnRdPYrD9cRk/ISe9cgkWWR7WDTd2K4fpUOlJFStBcxafTdjM2LAOGTPaQlRr40BwT3Uy/5ZrD9Hc8WnOdlXLvgwqbjlZwvix7XF1ADdaK4VtkSCclg2cmOHGfyeddFLd48G2xPxT6U+AzzGFkx70H66Eth/sA/mq8DyOJSwLQX6tOM7QOoSbyjFvzBfbCQE3y8zNBiVJkiSpNXRpyjf6k6ROjhtnEXbdc889dYfGoyuqPWkNQADW0RCMUilb603OGlEKWgkxs8F1R8bJl8GDB9fUmqQjjAehMVXeb7zxxkg9uTuKESNGxJNwvfrfELp279HesyN1SMMH9W3vWZAkSWqz7wbcGJqipUq84ZukhjNkyJBYVWfwWzuCcto0dDS0RuAPWi0Vzo2IG6/RC5eKY6qEO0vw2xnHg+p/wt+OGvxKkiRJUnMY/kpqOPTV5KHaZdtzdCS0XqClyOiKdh30lablxE033RRGd605HrSskSRJkqRGY9sHSZKkTsC2D1J1tn2QJEmjgxF1tH3whm+SJEmSJEmS1IAMfyVJkiRJkiSpAdnzV5IkqRMZeuSqVS/tkiRJkiRY+StJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw19JkiRJkiRJakCGv5IkSZIkSZLUgAx/JUmSJEmSJKkBGf5KkiRJkiRJUgMy/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkiRJkiRJkhqQ4a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSZIkSZIkSQ3I8FeSJEmSJEmSGpDhryRJkiRJkiQ1IMNfSZIkSZIkSWpAhr+SJEmSJEmS1IAMfyVJkiRJkiSpAXVr7xmQJElS7eYeeF/o2r2HQ6ZOZfigvu09C5IkSaMlK38lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSZIkSZIkSQ3I8FeSJEmSJEmSGlCnDH+POOKIMP/887f3bKiCyy67LEw00USOUcYjjzwSunTpEn744YfRZlxY3sGDBzf0djV8+PC4nC+//HLF9/Xp0yf079+/XcevrW2zzTZh3XXX7TR/E/zb0jG4HiRJkiSpwcLfp59+Oowxxhihb9++oaOqNeBpCzfffHMMkiaccMIw3njjhXnnnTccddRR4bvvvgudCcvAmOYff//9d5vOR0sFbAQWaRm6desWZphhhrDPPvuEn3/+ObTHuA4aNGik19jHeI15bQuff/55WH311UNH0trjUy7kv+WWW8LRRx8dGsmFF14Y5ptvvngcIpRfYIEFwvHHH194/YwzzoiBfUdUar/ff//9w0MPPdTqn/3111+HXXfdNUw33XShe/fuYcoppwyrrrpqePLJJ0Nb4Nh0+umnt9jxLvuYY445QkfC9sj/vzjppJPae1YkSZIkafQNfy+++OKw5557hsceeyx89tlnodH9+eefzf7dQw89NGy88cZhkUUWCffcc08YOnRoOOWUU8Irr7wSrrzyytDZ7LjjjjEgzD4ITltyzNpS79694zJwsuCEE04IF1xwQdhvv/3afD569eo1Uuj26aefxmBrqqmmarP5INQi3Opo2mN8JplkkjD++OOHzqbcvnfJJZfESua99tornhQjuDzwwAOLTnZwgqqjVWpXQojds2fPVv+cfv36hZdeeilcfvnl4Z133gm33357PCnx7bffturntvRxNB3vso8nnnii2dNrampq8ZN/bKdsl/wrSZIkSaOjdg9/CQquv/76WAVF1V2pKjEq9KaYYooYnGy//fbh999/L7x2//33h7HHHnukKru99947rLDCCoWf+UK6zDLLhHHGGScGPwQWv/zyS1El1HHHHRe22267+DlUZBHcJTPOOGP8l8o2qpv4ol7uUm4uc+Zy5+y0qfjbaqutwgQTTBB22mmnmuYp77nnnovzSNhLFdOSSy4Zp73yyivHauCtt946vu/9998P66yzThwzwgyC4gcffLBoWuecc06YddZZ49jxvg022KBiVRiXVGerIU899dQwzzzzhHHHHTfO+2677dasCtcePXrEgDD7GJUx++OPP2L13jTTTBPnbbHFFouVmOXwOVhvvfXiek0/49xzzw0zzzxzGGusscLss89eU7hOcM0yTDvttDGk33zzzWOwUwpBz6abbhrnlXFgPK+99trC61dccUUMolim/Pa15ZZbVpyPNddcM3zzzTdFlYQETausskqYfPLJi97Lci288MJxu2feN9tss/DVV18VXv/+++/jckw22WRx3NluLr300kKYtMcee8TAlG1p+umnL6r8zFZXsr0edNBBI1VAjjnmmPHET73rj4C9a9eu4YUXXih6nm2X+fj3339bZHxKVYgSaJY6VjFPyy+/fPzviSeeOP5uOhbkjxVpG2cbYFlZ5v/+97+hEsZvttlmi9vLTDPNFAYMGBD++uuvusaDE0ZUY3NsYN9nW2IsEuaTdcq8TjrppLEitRS264022igek2eZZZYYBLIsxx57bNm2D0ybE31Mm/Hh86keZh/edttt4zbItDixVanVB+uDsS3n+eefj8dF5p8Aernllgsvvvhi0diX2u/z7QYYM66qYH/mJAav3XvvvSNdEUJVN+ud9UIlNFezlMPfqscffzyeHOJ3WDeLLrpoOOSQQ8Laa69deB/T5RjEumK/Y33fdNNNRdN67bXX4t85XudYwXEyexxO4886mXrqqeNxjHXw4YcfxqsSUrUueG6ttdaK64XtkfV59913h1qOd9kHY17rsSVVybO+F1pooTjG+fCYYwPHiC+++KLoebYh/hZU8uijj4bffvstrsMRI0aEp556quh11u+JJ54Ytzk+m7/72e33k08+ids0J24YE5bl2WefLbx+2223hQUXXDAe+1g/Rx55ZCG8Jshme0rV3Yw/f69q+RssSZIkSQ0V/t5www3xMlG+lG6xxRaxOocvTdnX+QJF6EmoQcjEl6ZkxRVXjMEA4Wfyzz//xECZwCqFoauttlqstnr11Vfja3zBJODIIlTlyx0VWYSZBNJvv/12IXgFISrVTXzZr8fJJ58cQwGmTWBTyzyx3Nkw8uqrr46BDfNWSgpI+PK/xhprxCpGPo/P4Uv9Rx99FF9nHPkSyhdilo8wY9lll61reQiZzjzzzPD666/H0Ozhhx+O1VXlpJCkUhDbEmPGfxO8XHfddfE9G264Yfydd999t2xIBMJM1mv6+dZbb40nEKjaJSzbeeedYzg1ZMiQOkYpxFCmXLUdJzEIPO666674GQQ3BHFpW2Pe2Zaz4THBCe/nJEWlcSWwZvtPIW0K0dLvZREeEkJSPU6oxjSzJy8Y+zfeeCMGNG+++WYMpFLAwzbA/LGfsi2xjWa32Szmh/WS3b9Zh4QiKcSpZ/3xOSuttFLRMoKfmX+20XLqGZ96cEIiHYsYD7YpWh+Uw0mctI0ffPDBcZt74IEHyr6fEI35ZH0wXYLT0047rebxIHgkLOQkFscB9v0vv/wyhrhZ7NOMEeH4eeedF59jGumkFwjznnnmmRga1oNps/2wnRMEc5xlPXNygICWAJ794Ndffw3N9dNPP8WTYRwfmEdCNo6JPF9pv89jjPm7wLGI7ZEgnIA2vz1yRQYnLaiAJpwnMMxWsLKPppMFHMN5sK/lT+zkse9xvGPfZHvdZJNN4j4IAnPmh7CW+b/xxhvj36f83zX+DrAtsl3deeed8W8XYTbH/1Sti9133z3OD2EroTLhNPOZsH3V2w6l2rElYdvnJC/LRhujLP42EaxmT74xXY411fZXripiXRAe8y8/ZxG487npGHfNNdfEIDb9HeWkAVcEcIxjGfgbl06iEOBzcpJ9lt89//zz4zpO4THHAfZNnmd7Yfk5wVfv32DWCcF19iFJkiRJnSr85csYoS8IeX788cdYrZOtWqOyjAcB8THHHBPmmmuuwuv08uMLMV/asl92CTn40gwqEfniTKUQIQAhA6EVlZXZKmLCAYJVqoCosCOgSGEfVY+guorQg0qgehC4ECRSScqjlnni83lvwhdIvgTzRbYSwiTCyrnnnjtOmy/fTCeFiITAVDFR/UjVGUFQtiKpFsw3VWsEAiwb64UAsBzmmfVHZVwWQX4KQ3hkWyTUO2YsF2EOIQhhIr9DILP00kuPFIglab0SnLNe08+EPYQUbA+EOfvuu29Yf/314/O1+t///he3y2wFehaVnswf1YSsV4Iw9oE0jgTHVMpl5/2qq66KlWQphCs3riAYYVqERAQ67Fus81Lvo7qQeVh88cXjmBL0pgpCxpVthBMjKWDkZEJ6jXXBGLMt8S8hSykEjLR1yVb2MT68n3CsOetvhx12iNXSKUQjPCS4IqivptbxqQfHo3RsoIKYbYrK03KWWmqpGHyxjbH+qf5LYW4phx12WKHin3XA+GT3u2rjcfbZZ8d1yck0Trrx35xw4zhH+4GEdUpFJNsWD3DijW0vGThwYNxvmBfew/7CvFSquE7HJ5aDzyB8o/KRYx0tYHju8MMPj1XxhK3NxT7H3xWWcc4554xXcRAmp78t5fb7PPZ3/hbwN4ZlJBBlf81fGcF64MoV1iPVnwTi7733XuF1fjdtB1TLEhISgvP5bAP/+c9/Si4voTjrlOlyHGcfPOusswr7Dsc9jn8c61lm1i8hKYF+wrH+oosuipW8PNg+2U5TNW662oL9j3khoORYwL6QDSTZH7NVvWDbyh6/eeyyyy41H1sSQlAqtfmMUn9b+fufPQbccccdcdnzJy2yCEmplE7//4J/2T7TZ3MigHCf7ZwTBXw2xxrGO40vVyYQ2vI8/7+Az1tiiSXi66xn9l1+l+Vj/llHhL1pPBlbjpfsN1R3s43X+zeYv3tsO+nBCSZJkiRJ6jThLxUvVH+lsIgvxVwqn63OoRKIS7+z0pevhECQysfUL5iKIL6Ip0pYKnb4sp39gkrFFCHFBx98UJhOtuKIMIovbtlLVEcFX9qzapknKriyNx/KVkxWwpdbwghCD8aAaTOOqfKXL6l84eQLKxV2jFe9VXZUmFF1TYBJiMB0CGzKTYf3vfXWW/ELcH7dUS2XHoRBzR0zgggqZQlKsu8h8KFquB6MF0FIFj+nqrtyUhhCcMuysq0SyJTCvBIWELYQePB79913X2E9gbCA1iZUn4HlJ2RLl2qXG9cUshGmEYAQ8LGOSvVTJqQmSCSgYF1S7YY0H1RmUolL6EXlW/bSaeaF9Ua4RXjBvJZDwEZVJ9sbWGdU+aYK/easPy5pJ8iiUjuNTzopUU2t49Oa8scyfq60jVEpzXbIsYmxIUTNbi/VxoN9iKA3O77pBl3ZMaYivVQIRdCYEAaz/lhvVD9S6UoQxgmMSgFw9jjLvHJCLVVEIlVejsqxl/AzhckEZrSO4biYHatqCA/5m1LLcSC7TKlndHb+2UdpMZFwYpJpc0KO8eLvF+0D8q1EKm0f/Ms2TIiYnTfGPl2xAsaWKu5q2H85icc0CPbzYTR/i/JVxez32eM3D4LcWo8t5Y71eRxnCNOp4gbjRBCbXfY8ToIQ6DJG4PjF3z32oTR+nCTh71gpLAuhbLkTvexLLGt2X0o97Pk7SHBPywn+zvI8+2SqBq/nbzB/EzkxlR4ff/xxxbGSJEmSpLy2TTpyCHn5MsRl39mAk/54BGaVKuay6GnLlzwCKoIqvmRlv0TzpZ9K2FKVNdlKtnxFLQFbtSo2LqXOh7KpB2dW/ktqrfOURShG1STTr1T9S/DLJb5UrVGtRBBJRWFqP8CXcCoCCRwI66i043JeLh0mLK62TFy6S8USY80lrnw5Zr6ozuIzSlWhlsM6Zh5LqXfMCCsIkwgc+Dcre/lyayIMIdAhRGS7rhS6cMk/lWdUEab+yVQ1Z9tEED4QXhC6EZzSZoO2D7Wi8o4+slyWnNpJZKVLx3kQQBDQEszwc5oPKveoZKT/J9sVYQmXiLN9EVgR4lLNxwkBAhkq3fK9SROCXtYf1YtU1rHcKfhj/da7/hhfLr2mKpDKbKZZqc1CveOTjgO17OOtLQXlVByyfth3OObRlqDW8WCMCeOoYM3L3uiuUqiWR9UpD6rkqfqkapvAPvU+zit1nM0+l05spGNvrcfYLEJoTkax7IRs/E0hOG2tG0dWmv9yqHgmBORB2wEqTgldS7VFGBW1rks+n+2K4wt/Fwj72baoSC+H7a3c8buWY0ut80gVPdst2zX99zneVGshxP+/4HiZPaHDOuFED3+r+LtYSbXX2ZfYF9nPSq1bKnQJ4Tkuctxk/+CYz75R7W9wFttuR7xppiRJkqTOo90qfwl9CbT4cpmtGqKahtAs3fiK6tXsDVaQqn+yCEX4gsnloIQFVP4mBFSEO3xJzT9qqYhCeh+ViVl8oU09E9Pr9G+tpjnzRAsAvnBmex5npZve0aeTAIFKM4I1qgQJbLP4QkxIxyWvhKa8Tt/eUstEBVy2Qppwji/RrDsu5SWUTlXXranamBGUMv5U3OVfT5c2lwtu8uuV7S57MzDwc7blSKUwhErLatsW0+PGfFyOTMBLFVj20vtsKMPJDIIP1lk9l/2yzVCZSThXat6pSCQko+8loR1VoKUqLtkmCNRoO0FYnb0ZIlWVVOzTf5aqOnpdfvfddyXnh+Xlcm16XBJMpqpfNHf9MT4ELOwXHFdKhTHNHZ9S+wPtVypVypc7VpSSP5bxM9teKVRcE2TSX5ZKSapaS/XbrTQe7EMEYmyf+TGuJ/AtJ41hpRtX1ovx5xL97DT5W1Ft3+IkA618aHVAeJa9qV25/T6L7Zq/Rc05DjQH08yPW6Xtg3/5e5n9HeaNv3+pVUelbbTUsnNsIcCnLzAtd9inm6vWY0ut2K45vnDs4WRvviI7i32avrqEq9n/f8HPnERh3th/CHizV9fkq7n5nXLHMvYlwt1Sf49Sv3GmT2hNu4v02cxbtb/BkiRJktQQlb/ceOb777+PFTj5Cl8uiaVqhy+hXE5MkEnYwZc9Al7CC4KyLEIkKmeoRKXKNVspQ89GQkouWeULJCEHISLVOOUuyS9VecQXOUIrbpZDZQ/zTZ9F+sFSLcUX0lNPPbUQwlbSnHmi/QWX3fOlnDYAhLuEE1wOy02Z6EvIePGlli/vfOmkCo2qsmwVGmM/bNiw2M+RmwVR0cnrKTBgmQgb+X2qkKhKylZi8uWWyjuqN3lP9qZQranamBFCsx1Q+UgwTZhIz0a+3PNFPntCIIsgjPewfbHdMCYHHHBArGJlGnxB56QCY0qo1lJSywFCPT6TbYfL1fPBEgEl1dwEMdnL7mvBdAkuy1WKUzFNEMS6ZH/jxAWtKLJY/7QBIETjMmm2nxRAMc9UjDJOBB706yWozVevJawzWhOwTXLZdbY/cHPXH/PCdsH2QSVvtYq9esYHqZcqlaMEZnxOpfcT0LLfMU6Ej8xPucpl9h3CH8aE7ZjxK1fZzfZC5STVvlztwPtSe4dax4OKbbYjxp1jCVX7HD+YJn1h8xXXlVD5z/GH8eGYyDjSNoCwNt+uYFRw3ONqAvriEuhyMjDfHqHUWNH7lr8bnLxif85vF6X2+zx+j2pcju20DeAEDIFgal3SHASitARg3bBdUwVKUMl2wMmRLLYHloFjO59JdXpqi8S+wrxxUoa/fewrVOnSRiC1ziiHZafPNb2MWXZ6+XLVAVX+7If8baY9SLkTEQknF7744oui59j2+fxaji31oGKYQJ5tLNtaohTGiFY4pW6ixr7D61Thso+wHzCfbAeMIf//gv9fwj5Cb2z2TaqgOc5xY0a2ebZvjotcAcNy8v85OP4RxrOczCPbKMeLtP1y4oxtkONDtb/BkiRJktQQlb98+SJUK9XagfCXL8NUw1BRSFDEFzQCKCrdCB3yCCT5ssfvZKsJwRdsLrWkqpIKJEIlvrhl201UQ5UO1TvczIXfS1/S+QLPl28CK/oZEkqXu9y53nniC32+dymXa1MxSQDCl2ECOcJnpsd8pECOL5TcGIpwlvdRpZQQzBFkEtrw5Z7glkprppV6DLIsfLElcOPLb/bGc1Sp8hnMCxWThBJ8Oa6EqiZCgWqX6o7qmBHOsC4IyPkizbxzKW25VhogaCR4o+qNaYLf45JxWhswLqx3pp1utNYS6NfKemH9MF1CUz43j32EfYIAMf96LePK+i5X1UlQR0hByEToTJVe/qZ2BCNsE4w/YQUBIWEhCK4IrQioCFWYH4KMVPlWCvsnIQnrML9emrP+kFqOsD/Wq9L4pO2DbYP5TUF8pdYm9GFON4MiBMv3Sc1iOTnWsd0RGLFfsT2Usvbaa4d99tknTo8gkpMGHBvrGY9UyUooRRsRrgwg9EvtXirhJFx2++f4TSUqQSaBIdsoJ8UIVOnj21IIqAnO2K6YX45VHBur/X0hwGT/IgwlNOYEXrX9Po/f4/jKeuKzOflHWxfC5Xqwj6bAmv2YQJAb+7E/cQxlPdIXNn/ij+2IfY19jxM/LHs6OcQ2SI9wKlPZ9wggaclSywlNwlP2VY7r6WZ3bBOcHOBvAn2IWafZq0z4W5Qfd4JSQtHsg3Cz1mNLPdg+2QaZT44R5bDds72kG77m8TxjyQlMxp11y98Rlpv/v5Gqkznu0ZKB7YaTOKx/liGdIGE/JcTlPYw/J1xYp2n52ac40UKozPrjxCEnEdk3qv0NliRJkqSW1KWp1ruIqc0R5mZDg86MKjIuP6faqVSFnSoj1CEY4ASE4zoyKgoJmfI3qerICNMIXnl0hvHghBAntqoFrypGyxyCVK5SqCc05thPZXepE0JtjTYnhJb02m3JE2D14qQG1bkE8KMrKtk5Idir/w2ha/fa++tLHcHwQaWv4JEkSVLzvxtwY2iukuywN3xTeWTyVHNyI7VGQNUel20b/NaH6kW2Ax6lej2P7uNKD2wqGKl2pHJ2dNda48Efk/fff7+umw3q//bRnXbaqe5q4Y528o4q1fYKftn+6JXLVS+jc/ArSZIkSc1h+NtBUfVV6mZOnRX9FVU/LkcnAKbFRql+kKP7uNICgculqY5sTsuHRtNa48HZxE8++aTFpjc6oZVCZ0f7n3I9t9sCbZbod0zv4JVXXrnd5kOSJEmSOiPbPkiSJHUCtn1QZ2bbB0mSpPZp+9BuN3yTJEmSJEmSJLUew19JkiRJkiRJakD2/JUkSepEhh65atVLuyRJkiQJVv5KkiRJkiRJUgMy/JUkSZIkSZKkBmT4K0mSJEmSJEkNyPBXkiRJkiRJkhqQ4a8kSZIkSZIkNSDDX0mSJEmSJElqQIa/kiRJkiRJktSADH8lSZIkSZIkqQEZ/kqSJEmSJElSAzL8lSRJkiRJkqQGZPgrSZIkSZIkSQ3I8FeSJEmSJEmSGpDhryRJkiRJkiQ1IMNfSZIkSZIkSWpAhr+SJEmSJEmS1IAMfyVJkiRJkiSpARn+SpIkSZIkSVIDMvyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV5IkSZIkSZIakOGvJEmSJEmSJDUgw19JkiRJkiRJakDd2nsGJEmSVLu5B94Xunbv4ZCpUxg+qG97z4IkSdJozcpfSZIkSZIkSWpAhr+SJEmSJEmS1IAMfyVJkiRJkiSpARn+SpIkSZIkSVIDMvyVJEmSJEmSpAZk+CtJkiRJkiRJDcjwV9Ioueyyy8JEE03kKLaCLl26hMGDB482Y9unT5/Qv3//Fpve8OHD4xi+/PLLoSOZYYYZwumnn17xPUcccUSYf/7523X8GvVY8sgjj8Tt4ocffugQ05EkSZKk1mT4K41mnn766TDGGGOEvn37tkhotfHGG4d33nkntDbCMIKW/OPBBx8MbamlArYUHKXHFFNMEfr16xe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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Top genres success(reviews) without splitting them\n",
+    "\n",
+    "top_genres_combo = steam.groupby(\"genres\")[\"success\"] \\\n",
+    "    .mean() \\\n",
+    "    .sort_values(ascending=False) \\\n",
+    "    .head(10) # Only top 10 genres\n",
+    "    \n",
+    "top_genres_combo.sort_values().plot(kind=\"barh\", figsize=(10,6))\n",
+    "\n",
+    "plt.title(\"Top successful genres\")\n",
+    "plt.xlabel(\"Average Success\")\n",
+    "plt.ylabel\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1481eb26",
+   "metadata": {},
+   "source": [
+    "While Free-to-Play and multiplayer games appear among the highest-performing categories, this does not imply that monetization model alone determines success. Free-to-Play represents a pricing strategy rather than a game genre. \n",
+    "\n",
+    "SABINA COMMENT: \"Free-to-Play represents a pricing strategy rather than a game genre\" What do you mean here? As in, you can do purchases in-game or you have a Freemium model?\n",
+    "\n",
+    "The results show that high-performing games typically combine Free-to-Play with engaging genres such as Action, RPG and Massively Multiplayer. This suggests that success is driven by the interaction between monetization strategy and gameplay design, rather than by pricing alone as concluded in previous hypothesis.\n",
+    "\n",
+    "Steam should promote games that combine strong engagement mechanics (such as multiplayer and action/rpg systems) with effective monetization strategies(Free or upper mid pricing).\n",
+    "\n",
+    "SABINA COMMENT: I would try to separate the monetization strategy from the gametype in the analysis and see what happens when we only look at gametype.\n",
+    "\n",
+    "--------------------------------------------------------------------------------\n",
+    "--------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5214adc0",
+   "metadata": {},
+   "source": [
+    "### H7: Publisher performance\n",
+    "\n",
+    "(Who creates successful games)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "377041fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    14353.000000\n",
+       "mean         1.885390\n",
+       "std          4.697589\n",
+       "min          1.000000\n",
+       "25%          1.000000\n",
+       "50%          1.000000\n",
+       "75%          1.000000\n",
+       "max        212.000000\n",
+       "Name: count, dtype: float64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "steam[\"publisher\"].value_counts().describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccd3871b",
+   "metadata": {},
+   "source": [
+    "~14k publishers out of 27k games which gives a ratio of about 2 games per publisher, this means many publishers have published only 1 or 2 game in Steam which also indicates that we have few top publishers that might dominate the platform (as seen before in H4)\n",
+    "\n",
+    "SABINA COMMENT: I'm a bit confused as to what the publisher variable is"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbdcc78a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev_counts = steam[\"developer\"].value_counts()\n",
+    "\n",
+    "# Keeping only developers with at least 5 games\n",
+    "valid_devs = dev_counts[dev_counts >= 5].index\n",
+    "\n",
+    "steam_dev = steam[steam[\"developer\"].isin(valid_devs)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "9ca369d2",
+   "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>success</th>\n",
+       "      <th>total_reviews</th>\n",
+       "      <th>average_playtime</th>\n",
+       "      <th>review_score</th>\n",
+       "      <th>num_games</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>developer</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Valve</th>\n",
+       "      <td>86672.115385</td>\n",
+       "      <td>95159.884615</td>\n",
+       "      <td>2663.538462</td>\n",
+       "      <td>0.890779</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bethesda Game Studios</th>\n",
+       "      <td>47296.900000</td>\n",
+       "      <td>56177.400000</td>\n",
+       "      <td>2001.800000</td>\n",
+       "      <td>0.827713</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tripwire Interactive</th>\n",
+       "      <td>28663.000000</td>\n",
+       "      <td>31387.200000</td>\n",
+       "      <td>1059.600000</td>\n",
+       "      <td>0.851455</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Klei Entertainment</th>\n",
+       "      <td>19098.000000</td>\n",
+       "      <td>20019.571429</td>\n",
+       "      <td>621.714286</td>\n",
+       "      <td>0.895675</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ubisoft Montreal</th>\n",
+       "      <td>19070.157895</td>\n",
+       "      <td>23274.473684</td>\n",
+       "      <td>1027.368421</td>\n",
+       "      <td>0.785946</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Obsidian Entertainment</th>\n",
+       "      <td>18309.142857</td>\n",
+       "      <td>19555.714286</td>\n",
+       "      <td>1331.428571</td>\n",
+       "      <td>0.844867</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCS Software</th>\n",
+       "      <td>17348.333333</td>\n",
+       "      <td>18161.250000</td>\n",
+       "      <td>648.500000</td>\n",
+       "      <td>0.764742</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bohemia Interactive</th>\n",
+       "      <td>15939.812500</td>\n",
+       "      <td>22225.187500</td>\n",
+       "      <td>2260.187500</td>\n",
+       "      <td>0.657844</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avalanche Studios</th>\n",
+       "      <td>14870.666667</td>\n",
+       "      <td>18750.166667</td>\n",
+       "      <td>735.166667</td>\n",
+       "      <td>0.740329</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arkane Studios</th>\n",
+       "      <td>12659.000000</td>\n",
+       "      <td>14036.285714</td>\n",
+       "      <td>591.428571</td>\n",
+       "      <td>0.806615</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             success  total_reviews  average_playtime  \\\n",
+       "developer                                                               \n",
+       "Valve                   86672.115385   95159.884615       2663.538462   \n",
+       "Bethesda Game Studios   47296.900000   56177.400000       2001.800000   \n",
+       "Tripwire Interactive    28663.000000   31387.200000       1059.600000   \n",
+       "Klei Entertainment      19098.000000   20019.571429        621.714286   \n",
+       "Ubisoft Montreal        19070.157895   23274.473684       1027.368421   \n",
+       "Obsidian Entertainment  18309.142857   19555.714286       1331.428571   \n",
+       "SCS Software            17348.333333   18161.250000        648.500000   \n",
+       "Bohemia Interactive     15939.812500   22225.187500       2260.187500   \n",
+       "Avalanche Studios       14870.666667   18750.166667        735.166667   \n",
+       "Arkane Studios          12659.000000   14036.285714        591.428571   \n",
+       "\n",
+       "                        review_score  num_games  \n",
+       "developer                                        \n",
+       "Valve                       0.890779         26  \n",
+       "Bethesda Game Studios       0.827713         10  \n",
+       "Tripwire Interactive        0.851455          5  \n",
+       "Klei Entertainment          0.895675          7  \n",
+       "Ubisoft Montreal            0.785946         19  \n",
+       "Obsidian Entertainment      0.844867          7  \n",
+       "SCS Software                0.764742         12  \n",
+       "Bohemia Interactive         0.657844         16  \n",
+       "Avalanche Studios           0.740329          6  \n",
+       "Arkane Studios              0.806615          7  "
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dev_analysis = steam_dev.groupby(\"developer\").agg({\n",
+    "    \"success\": \"mean\",\n",
+    "    \"total_reviews\": \"mean\",\n",
+    "    \"average_playtime\": \"mean\",\n",
+    "    \"review_score\": \"mean\",\n",
+    "    \"appid\": \"count\"\n",
+    "}).rename(columns={\"appid\":\"num_games\"}).sort_values(by=\"success\", ascending= False)\n",
+    "\n",
+    "dev_analysis.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f54d79f7",
+   "metadata": {},
+   "source": [
+    "The most successful developers are not one-hit wonders but studios that consistently produce high-performing games across multiple titles."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "191ad7ad",
+   "metadata": {},
+   "source": [
+    "The analysis identifies a group of developers that consistently produce highly successful games on Steam. Studios such as Valve, Bethesda Game Studios, and Ubisoft Montreal achieve strong performance across multiple titles, indicating sustained success rather than isolated hits. These developers also exhibit high levels of player engagement and strong review scores, suggesting that both retention and player satisfaction contribute to their success. This reinforces the idea that successful games result from a combination of quality, engagement, and scale.\n",
+    "\n",
+    "Steam should prioritize partnerships and promotional visibility for developers that consistently deliver high-performing and engaging games. Supporting these studios can drive sustained platform growth and maximize user engagement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c57b56a",
+   "metadata": {},
+   "source": [
+    "SABINA COMMENT: As per usual, your data analysis is very punctual and good. To improve on this I would recommend you trying a shift in mindset from *formulating hypotheses* to *understanding the business, what works, what doesn't and what can be changed*. \n",
+    "\n",
+    "The idea is to move from a fixed structure (hypotheses) to something more fluid, where you start from a key question (\"What drives the success of a game?\") and you answer it by decomposing it, step-by-step:\n",
+    "1. What is success of a game?\n",
+    "2. How many games are \"successful\"?\n",
+    "3. What do those games have in common...\n",
+    "    1. ...in terms of price?\n",
+    "    2. ...in terms of owners?\n",
+    "    3. ...in terms of game type?\n",
+    "4. What about unsuccessful games? Same questions as before.\n",
+    "\n",
+    "Also, you can start asking yourself: \"Are there any other goals the company might have with regards to games?\" For example, maybe they want to spend less on partnerships with the big game developers and find some indie game developers to invest in. Whom should they invest in, then?\n",
+    "\n",
+    "You can also use Claude/ChatGPT to come up with potential business goals a company might have and identify how you can reach them using the data. I trust you'll do well ;)"
    ]
   }
  ],