diff --git a/Project_salaryprediction.ipynb b/Project_salaryprediction.ipynb
new file mode 100644
index 0000000..40e556c
--- /dev/null
+++ b/Project_salaryprediction.ipynb
@@ -0,0 +1,728 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Salary prediction **"
+      ],
+      "metadata": {
+        "id": "mTvXqnMZ4MSh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##EDA"
+      ],
+      "metadata": {
+        "id": "2lqN9fuRKZ1i"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cat_deg,num_deg,car_deg=degisken_tiplerine_ayirma(df, 10, 20)\n",
+        "\n",
+        "plt.figure(1)\n",
+        "plt.figure(figsize=(30,15))\n",
+        "sns.barplot(x=df.groupby(\"Job Title\").mean()[\"Avg Salary(K)\"].sort_values(ascending=False).head(15).index,y=df.groupby(\"Job Title\").mean()[\"Avg Salary(K)\"].sort_values(ascending=False).head(15))\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "0NVDgYj-KcW8",
+        "outputId": "1bf3b4fa-b0a2-4606-a998-52adb66b790e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset kolon/değişken sayısı: 41\n",
+            "Dataset satır/veri sayısı: 742\n",
+            "********************************************\n",
+            "Datasetin numeric değişken sayısı: 6\n",
+            "Datasetin numeric değişkenler: ['Rating', 'Founded', 'Lower Salary', 'Upper Salary', 'Avg Salary(K)', 'Age']\n",
+            "********************************************\n",
+            "Datasetin categoric değişken sayısı: 24\n",
+            "Datasetin categoric değişkenler: ['Size', 'Type of ownership', 'Revenue', 'job_title_sim', 'seniority_by_title', 'Degree', 'Hourly', 'Employer provided', 'Python', 'spark', 'aws', 'excel', 'sql', 'sas', 'keras', 'pytorch', 'scikit', 'tensor', 'hadoop', 'tableau', 'bi', 'flink', 'mongo', 'google_an']\n",
+            "********************************************\n",
+            "Datasetin cardinal değişken sayısı: 11\n",
+            "Datasetin cardinal değişkenler: ['Job Title', 'Salary Estimate', 'Job Description', 'Company Name', 'Location', 'Headquarters', 'Industry', 'Sector', 'Competitors', 'company_txt', 'Job Location']\n",
+            "********************************************\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2160x1080 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(2)\n",
+        "plt.figure(figsize=(30,15))\n",
+        "sns.barplot(x=df.groupby(\"Location\").mean()[\"Avg Salary(K)\"].sort_values(ascending=False).head(15).index,y=df.groupby(\"Location\").mean()[\"Avg Salary(K)\"].sort_values(ascending=False).head(15))\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 652
+        },
+        "id": "22Cx4FxWKmld",
+        "outputId": "26246a3f-da8a-4fdc-f427-1b5527716734"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2160x1080 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(3)\n",
+        "plt.figure(figsize=(30,15))\n",
+        "sns.barplot(x=df.groupby(\"Industry\").mean()[\"Avg Salary(K)\"].sort_values(ascending=False).head(15).index,y=df.groupby(\"Industry\").mean()[\"Avg Salary(K)\"].sort_values(ascending=False).head(15))\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 729
+        },
+        "id": "FbCwy4IsKnUT",
+        "outputId": "26b68aa3-eeb0-4577-8b3b-620d6a93ba74"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2160x1080 with 1 Axes>"
+            ],
+            "image/png": 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oVX06q5+9ymo7DGO3DF/r7vNXu1km00xqNyWW4Zq7yq8k6e5/Ul4uykHdff6uB9PP4eXnDMTedfd9pu1//61JV4t2QXc/f+4Q7JOvVtU1Mr1WqarbJnEDfhl+Osnjk1xYVf+Si94zHDpvLNb0e0n+OsmDpscPz2o15o/Nlojt+Pr0+XNVde8k/5Bkj6vZge+c7j5y7gzsm+7edT7wa6rqdUkO3l8nfSjANsuvzh2AffLY6fN9Zk3Bt8Nqk2V7W1U9JckVp/OITkjyJzNnYj3f2LryeTrLTXm5HGdX1X27+7VJUlX3S/KlmTOxhu7uqvrTJLZcW64/qaoTYjuaJXp8ktcmOaqq3pnk8KwOJWfDdfdV5s7At+Wo7v7xLY+fVlXvny0N2/Xr0yq+JyR5bpJDkzxu3kgwrqq6S3f/RVXtcZJAd5+805nYnqp6TJKTuvuc7v5aVV2pqk7o7ufNnW2nOQNsw0yHIu/aOui93f3FOfOwvqp65u77T+/pGptrWnmy9UBdM+MXYDqU/JFJ7p7VTNw3JHmRrWk2X1XdI8kLs9qCppLcMcnx3f2GWYOxlqo6KslJSb4nq/H7TJL/0N0fnzUYa6mqlyT5n7b/Xaaq+uQeLnd3245mAabV6jfO6nfnR7v763v5FjZEVd03F22h99buft2ceVhfVb07yRO7+x3T49sn+a3uvt28yVhHVR2869xZ4LJXVU/r7qdW1Yv38HR39yN2PBTbUlXv7+5b7HZtvzzPVAG2QarqQVnty/nWXHQj8Ind/eo5c7Geqjq9u2+127UzbaO3+aY3sv89q5u4X0xy/SQf7u6bzRoM9gNVdc0kt50e/lV3W0G0MFV1SJJ09/+bOwvrq6qPJLlREtv/Lsw08eOB3f3KubOwfVV1YJJ7J7lBtuzI0t3OEd5wVfWMrCarnjRdekiSU7v7yfOlYl1VdYskL0ly1az+5v1jkv/Y3R+YNRhrqaqPJ/lCkrdPH+/YX7fygp1UVUd29yf3do3NMx3Vc/Ndk8On16Bn7o/3OhVgG6SqPpDkbrtWfVXV4Une3N3HzJuMS1NVP5vVlmtHJdk66/0qSd7Z3Q+bJRhrm3727pLVz9stq+rOSR7W3Y+cORprmGZv/lpWxeVBuegmrlnwG6qqbtLdH6mqW+3p+e4+faczsb6qelh3v7SqHr+n593EXYZpy9Fv0d2f3uksbF9Vndrdx86dg+2rqj9L8q9JzkryjV3Xu/tps4ViLVV1ZpJbdPc3pscHJjnDxIFlqapDk6S7vzJ3Franqo7IaqL47ZPcK8k5u69uAL6zLmGy/2nd/QNzZWI9VfWsrO6T/e506aeTfKa7nzBfqnk4A2yzHLDblodfTnLAXGFY28uSvD7Jf0vypC3Xz3MOw2J8vbu/XFUHVNUB3f2Wqvofc4dibSdmtf/7aUkunDkL63l8kuOzWnm5u86qkGZzXXn67CyUBevuT1fVHZIc3d0vniZeHTJ3Ltb25qr6hSSvzGoFXxJngC3EdRUmi3a1rFYOJauVRGy4S5q4U1VJTNxZiqq6blbF1x2THJPkg0neMWsoGFhV3STJzZJcdbdzwA7NlqND2Gi/lFXp9bPT4zcledF8ceajANssf15Vb0jy8unxg7MqVthg07L7c6tq97O+DqmqQ5wjtQjnTFt4vT3JSVX1xWy5mcTGO7e7/a5ckO4+fvp857mzsH3dvWsG2fO6++xZw7DPquqpSY7N6hyiFye5XJKXZnVzic334OnzY7Zc6yRWP2++11fV3bv7jXMHYdv+W5IzquotWe04cKdcfAIkm+nSJu7YEmk5/i7J+5L8Rnf/zNxhYD9w4yT3yWrix49suX5ekkfPkohtmVasP3/62K/ZAnHDTK36HaaHb+/uP5ozD+ub9lbtrN4MHZzkyKwOtd7v9lZdmqq6cpJ/yWrF5UOzms15Und/edZgrGU6j+HAJCcn+dqu67bR23zTVkIvT/Kq7v7bufOwPVX1N0k+ldUKlJO7+5/mTcR2VNX7k9wyyem7DkJ2dilc9qrqR7Mqmw9I8vVctHXzobMGYy1V6xlwmQAAIABJREFUde2szgFLkvd29+fnzMP6qur23f3OvV1jM1XVMVndK7tTkiOSfCzJ27r7xFmDweCq6nbd/e65c7C+qnpVdz9oy33qi9kf3+8pwDZIVT2zu39pb9dYhulsmxO6+1FzZ2HvprNQju7uN1fVlZIc2N3nzZ2LvZtm4e6uu9s2ehtu+rl78PTxjayKlFdZObscVXWbJD+R5P5JPpTkFd390nlTsY6qem9332bXvv7TZJB3749viJZoeq3y+CRHdPfxVXV0kht39+tmjsZeVNUnk9wvyVntzfiiVNVLk7wtq4mqH5k7D9tzCefYfMs1Nte0a8sdstoG8WFJ0t17PNMU+M6Ytkl/dJIbZMtOct39iLkycemq6trd/TlnPl9EAbZBLuEFmZm4C1ZVZ3X398+dg0tXVY/O6jyiw7r7qOkm0gu6+64zR2MNVXXD7v7E3q6x2aafu19N8tDuPnDuPGxPVV0zybNj/BZjOj/q6CR3y2pbr0ckeVl3P3fWYKylql6Z1dmX/6G7v28qxN7V3beYORp7UVV/meSHp21pWJCqunNWN97vmOSoJGck+cvufs6swbhUVXW7JD+U5D8n+e0tTx2a5Ee7+5hZgrEtVXVqkiskeVdWRxe8fX+8iQs7rap2/cxd7Mz17n7NbKFYi4U2F3EG2Aaoqp9NckKSG07bQSWrrTAOSWI5/kLsdqjuAUluleQfZorD9jwmyW2SvCdJuvtjVfVd80ZiG16d1c/bVn+Y5AdmyMI27bYK7MIkvzhvItZVVYcm+dGsVoAdleSPsvpdygJ0929V1d2SfCWrPf7/S3e/aeZYrO+o7n5wVT0kSbr7n6uq5g7FWj6R5K1V9fpcfOvmZ88XiXV091umAvPWSe6c5GeS3CyJAmyzXT6reysH5eLngH0lyQNmScS+OKG737v1QlUd2d2fnCsQ7CeutD8WJoO4W5Ldx+6ee7g2PAXYZnhZktdnNft26yG653X3P84TiX2w9cX0BUn+NIkZEcvwte4+f9d9o6o6KA5E3nhVdZOsbjpcdTo/cZdDszqHjw1XVe9Jcrkkr0ryQKv2FucDSf5vkqfbF36ZpsJL6bVM51fVFTO9Xqmqo7KlTGGjfXL6uPz0wUJU1SlJrpzk3VnNhr91d39x3lTsTXe/Lcnbqur3rRhatBfkWyc9vjomPcJl7XVVda/u/rO5g7CeLQttjtqy0CZZ3bfeLxfaKMA2w9eTfLa7H5IkVXXjJPdK8ukkJ88ZjPV199OSb86Ib+dHLcrbquopSa44zYY/IcmfzJyJvbtxkvskuVqSH9ly/bys9qhmg1XVAUlO7u5nzp2F7auqA7MavyfMnYXtqarzsudJHpXV65dDdzgS++bXkvx5kutV1UlJbp/kp2ZNxFq2vGe4Unf/89x52JYzs7rZ/n1Jzk1yTlW9u7v/Zd5YrOn3q+pb/v45N3izmfQIs3tskqdU1deyun/tPcPms9BmN84A2wDTNgqPnLZdu1GS9yY5KclNk7yvu590qf8DbISqOjbJi3PRSrBzsxrXU+dLxTqmG/GPTHL3rP6YvyHJixxMvgxVdTurT5apqk7t7mPnzsG+mW763W7uHLC/qqprJLltVq9d/qq7vzRzJNYwnUd0YpJDuvuIqjomyU939wkzR2NNVXWVJP8xyS8k+e7uvsK8iVhHVW1dKXRwkh9PckF32357g1XV/ZLcP8l9k7x2y1PnJXlFd79rlmAAG66qjtjT9e7+u53OMjcF2AaoqrO6+/unf//XJId192Oq6vJJTtv1HJttWlb6mO5++/T4Dkme1903nzcZ21FVhyW5bnefudcvZlZV9Yvd/ZtV9dzsYTVDd//8DLHYhqp6RpIvJXllkq/uur6/zkpamqp6fpLrZHXm3tbxs3p9g01/5y6Rn79lqKpTuvuue7vG5pm2/31Aktd29y2na3/d3d83bzL2pqp+LskdsloF9qmstkF8e3f/xZy52HdV9d7udn7pBquq/9PdD6+qp3T3b8ydB/Y3VXWnPV3v7r/c6SxsT1WdldW9sspq4seRST7a3TebNdgMbIG4GbbeuL1LkmclyXQm0TfmicQ+uHBX+ZUk3f2OqrpgzkCsp6remtWMsoOSnJbki1X1ru5+3KzB2JsPTZ+tslyuB0+fH7PlWie54QxZ2L6Dk3w5q9cuu3Rs37zpTstFb4R25+dvw1XVwUmulOSaVXX1XDSOh2ZVSLMA3f2ZXWfPTi6cKwvbcoUkz85qkqr3eQuz2wSQA7IqMq86UxzW9wNV9T1JHjxNvrrYL08Td+Ay98Qt/z44yW2yej9h+9gNt/uCmqq6VVZHvux3FGCb4cyq+q0kn01yoyRvTJKqutqsqVjL9AskWZ0j9btJXp7VDaQHJ3nrXLnYlqt291eq6lFJ/qC7n7rbQZFspv+fvfuOkrOs3z/+vhJ6CUVAinQFBCX0XgQUf6CgdBEUQQUVEQsWRL8oKCiIiig2iqCoSBVQEem9BAJBKYIoolhBeg25fn/cz5ghJruzIdn7mez1OmfPznNP1nN5ht2Z5y6fz87ABbZPkbSX7VNqB4qhsb187Qwx/Wyn31Afyu9d39sP+DCwJGXyoTMR+BjwzVqhYkgekLQRYEmzU3pr3Fk5U/RmrO2vdA90TqfUChRD0r0BZCLwR0oZ/Gi37wCXUDbo3DLFc9m4EzGT2e7ut46kpYGvV4oTL4HtWyStXztHDSmB2AKS5qbc+CwBnGT7tmZ8I2BF2z+smS8GJumyAZ52muq2X3MseGvgFOAQ2zdJmpDyle0m6Rbba035OPqHpHmAjwLL2N5X0quAlW1fUDla9EDSSsC3gZfbfo2k1YHtbX+hcrToUXOC6FV0NZFPOZP+IOkA28fVzhFDJ2kR4Fjg9ZSJ+IuAA20/VDVYDGrKz5uSZgMm2F61YqyIEUHSt22/v3aOiJFO5Qj77/Le136SPtp1OQpYC3iZ7TdWilRNFsAiYsSTtAvwWeAa2++XtAJwtO2dKkeLAWQBrP9JOp2yG/edzQLKPMC1tteoHC16IOkKSkmM76aPTf9pTj0fCLwCuBXYALguG3f6R7NZbjm6qnrYPrVaoIhZlKSDgU8DcwNPMfnk5XPA92wfXCtbDE7SjgM9n96l/UPSWGDT5vLK9O2OmPmm6Lk+ClgD+JPtPeulil5IOrTrciKlf+lZtp+pk6ieLIBFvESS9rT9oylW1v/L9leHO1PESCDpn8BPKZMQuzWP/8v2h2rkit5JGmd7HUnjuxZQbrM9tna2GJykm2yvO8Xrd2sWMPtDc/p5XeB622tIWgU4wvaAE4XRDpJ+CKxIWbzs9I9y3vvaT9I3pjL8KDDO9s+HO0/0TtKRWezqP5JOHuBp295n2MLEdJP0IWBfJvea3YGyAJ3T0BEzkaS9ui4nUha/rqmVJ4ZO0jy2n6qdo6b0AIt46eZtvs9fNUVMt5Tx6lvdzVjHVUsRL8VzTRlgA0haEXi2bqQYgn83r1nn9dsZ+FvdSDEEz9h+RhKS5rR9l6SVa4eKnq0DrOrsZuxHcwGrAGc01ztRehGNlbSF7Q9XSxaDOUTSnsDytg9v+qAsYfvG2sFi2tKzdJbxHmB9208CSPoycB2QBbCImajpuT4HsFIzdHfNPNE7SRsCJwLzAcs0p2j3s/2BusmGXxbAIl4i29+VNBp4zPbXaueJ6fJ9mjJeALYnSPoxkAWwFrN9Su0M8ZIdClwILC3pNGBj4F1VE8VQ7A98D1hF0l8pE7h71I0UQ/AXSQsC5wK/kfQf4P7KmaJ3vwUWJ4vO/Wh1YGPbL0DpawNcBWwC3F4zWAzqW8AkYEvgcOCJZmzdmqGiN5IWoHz23KwZugI4zPaj9VLFEIjJJ55pHmsa/zYiZhBJrwNOoZTPE+Xefa/0De4LXwfeCJwHYPs2SZsN/COzpiyAtYCk85lcT/V/2N5+GOPEdLD9gqTdgSyA9ad5bN9Yenn+18RaYSJGCtu/kXQLpfeQgANt/7tyrOiR7fuA10uaFxhl+/HamaJ3tndoHn5O0mXAApQF6egPiwB3SLqRrpOzuW/oCwtRduJ2Jt3nBRZu7idyCrrd1re9lqTxALb/0+yKj/5wEmXzwK7N9TuAk4GU/u0PJwM3SDqHct/wFsrJhoiYuY4BtrZ9N/y3gtJPgLWrpoqe2H5girnOF6b1b2dlWQBrh6/UDhAzxDWSvgmcDjzZGbR9S71I0aOU8YoYRpKWBR6x/ajthyQ9BbwVWEnSN20/VzliDEDSdsAE253TQh8DdpJ0P2UR84/10sVQSVqKcnoPysmG6A+fqx0gpttRwK2SLqdM4m4GHNFsJri4ZrAY1PNN5Y/OPcOi5O9mP1nR9k5d15+XdGu1NDEktr/a/N3chPI7uLft8XVTRYwIs3cWvwBs/17S7DUDRc8ekLQR4OY1OxC4s3KmKpSy8REzRrN7ekq2veWwh4khkbQCpYzXRsB/aMp4dU3uRsQMJOkGYAfbD0pagzLhdySlLNTztt9TNWAMSNIEYAPbT0l6M/BVYHdgTWAX22+sGjAGJOlgyo3sYc31n4FHgDmAU2wfWTNfxEggaQlgvebyJtsP1swTvZG0B7AbsBalHNTOwGdsnzHgD0YrSLoO+Ljtq5vrjYGv2N6wbrIYiKR5KPcHzzfXKwPbAvfbPrtquIgRQNJJlM0eP2qG9gBG296nXqrohaRFgGOB11M2XV1E2bD6UNVgFWQBrAUk/cz2rpJuZyqlEG2vXiFWDJGkFZpyUAOORXt1yngBTwFvs31a5UgxBJLOoBzF/wXw4yl2eEaLSJrQeW+T9BVgku1PSBoF3Jr3vXaTdJvtsc3jk4C7bX+5ub7F9lpVA8aAmrKjm3Y1kR9ve83mVMMVtjepmzAGIulxpl46XZSNV2OGOVJMB0kLAa8C5uqMpZdGf5C0CrAV5XfuEtsjcid1P5I0FjiVUvJXwMPAu2zfVjVYDEjSlcC7bd8j6ZXAjcBpwKqUDQSfqhowYhYnaU5K7+fOPcJVwPG2U7Y5+kZKILbDgc33N1dNES/VmZTdgN3OIHVxW0vSGMob+VLAzymnUPanlPOaQPlgHf3jy8BelNN8362cJQbWXYR6S+BgANuTpqhPHe0kSfNRNgtsBRzf9dxcU/+RaJPO4lfj2GbsBUlzV4oUPbI9f+0M8dJIeg/l/u8VwK2UPpjXUd4Po+Vs3wXcVTtHDF2z0DW2uQfE9mOVI0VvFrJ9T/N4L+Antg9o+u/dDGQBLGLmmg041vZXAZpNc3PWjRS9kHQK5cTXI831QsAxI/H0XhbAWsD235rvKbfWh5pdgKsBC0jqbqA7hkwEtt0PKSUPrwPeCxxCmZjfwXbqwbecpMOBE7r+dv6RUk7oQvKBrO0ulfQzSq+9hYBL4b8lodL/q/2+Tpm0fQy40/Y4AElrkv6J/WA+SbN3SgnZ/gH8d3dnTg9FzHwHAusC19veormXOKJyphjAFCcv1fV4NmAO25lXabGp9C79MOld2k+6Tz1vCRwNYPs5SenBFzHzXUIpofdEcz03pZTeRtUSRa9W7yx+Adj+T3PPPuLkg1qLSNoAOA54NaUPw2jgyZQyab2VKaf3FgS26xp/nLKoEu21gu3XAkg6gTJxu4ztZ+rGih69xfZnASQtC5wPHGb7TEk31Y0Wg/gwpYfGEsAmnYl4YHHKQnS0mO2TJP0aWAzoLhv0d2DvOqliCM4Evivpg7afgv+WAP5m81xEzFzP2H5GEpLmtH1X09MmWmrKk5fNKej9gf2Ac6qEiqH4IuWkJU3v0j2Z3Lv0O0B6l7bbhKZk+l+BV1Im3pG0YNVUESPHXLY7i1/YfqLpzRftN0rSQrb/AyBpYUboWtCI/D/dYt8E3kYpm7cO8E5gpaqJYlC2fw78XNKGtq+rnSeGpDPp3in99JcsfvWV0ZKWAZYBTgTeb/tSlRp6+UDWYi4NSH86lfHxFeLEdLD9V8pERPdYTn/1h89SJgP/3Ox+h8l/Rz9bLVXEyPGXZuL2XOA3kv4DpBJIH2hetw9T7tN/DKw7EhvJ9yF3NnwAOwIn2r4ZuFnSByrmit68l3Jydjlg667XclXgK7VCRYwgT0pay/YtAJLWBp6unCl6cwxwnaQzKCfYd6bcB444KnNQ0QaSxtleR9IE26s3Y+Ntj8jjif1G0lHAFyhvBBcCqwMfsf2jqsFimiS9AHT6oIhylPsp0ki+LzTlTL5GKZl3B6Uk25WUXZ2jbL+nYryIiFZr+n29srm813ZuZCOGmaTNgQWAC22nBHBLSVqE0iN4N+Ak4Djbj9ZNFb2SNIFSquspSsn0nbrKN99he9Wa+SIi2kzSupTNqw9S5soWB3ZrNhJEy0laDdiiubzU9h0189SSBbAWkXQlpa7qCZQyQn8D3mV7bNVg0RNJt9peQ9IOlJKIHwWuzOsXMfM1p74OoJQwGQ98MZO5ERER0UZNA/mX01WRxfaf6yWKgUh6EvgXcDKlzP2L2P7qsIeKnknaB/g0pXfpP23/v2Z8TeArtreqmS8iou0kzU5p/wJwd1cLg+gDkhYD5upcj8TPnFkAa5Gmh80/KP2/PkLZDfgt23+oGix6Iul3tldrekmdaftCSbdlASwiYnCSFgKWtj2hdpbonaRNgFfZPlnSosB8aSYfETFtkg4ADqXc901qht2pABLtI+lzwDQnTmx/fvjSxPSQtBRN71Lbk5qxJYDZR+JEYEREryTtD5xm+5HmeiFgd9vH100Wg5G0PaUM4pLAP4FlgTttr1Y1WAVZAGsRSQfaPnawsWgnSV8C3kopgbgesCBwge31qwaLiGgpSZcD21N2wN9M+VB2je2P1swVvZF0KKVn6cq2V5K0JHCG7Y0rR4uIaC1J9wLrp3dURMRLI2mu9PCOmLk61a6mGEu7nj4g6TZgS+Bi22tK2gLY0/a7K0cbdqNqB4gX2WsqY+8a7hAxfWx/ilJbfJ3mOPCTwFvqpoqIaLUFbD9GaUh+arNh4PWVM0XvdqAsYD4JYPtBYP6qiWLIJG3b9AND0o6180SMAA8A6R8VETEdJN0k6aPNCb5LaueJGAFGNy0ngP+WcZ6jYp7o3fPNhqtRkkbZvoyygXXEmW3wfxIzm6TdgbcDy0s6r+upMcDDdVJFryRtafvS7kmjrvcGgLOHP1VMD0nz2368efxK2/fWzhQxi5utuXndFTikdpgYsudsW5IBJM1bO1BMl22B/5N0C7AB+dwSMbPdB1wu6RfAs53B9JGKiOjJNsAHgfuBgypniRgJLgROl/Td5no/4FcV80TvHpE0H3AlcJqkf9JsXh1psgDWDtcCfwMWodTm7HgcSC+U9tscuBTYbirPmUwk9ZOrJf0R+DFwJLBi5TwxHSR9AHgIOMv2xNp5YkCHAb8GrrZ9k6QVgHsqZ4re/ay5EVpQ0nuBfYDvV84Ug5C0PnCf7X8B2P6gpP8DDgT2rxouYmT4c/M1B9lBHTFsJL3b9olTjH2pqeQSLSXpZOBztu9vhhYAdgGOAtI7MWLm+ySwL/C+5noCsHi9ODEEbwGeAT4C7EH5+3lY1USVpAdYy0haltJM/uKmHM1snRMpETFjSZqHcoJhYtfY+4FvAm+zfUa1cDHdmiatqwDL2t6+dp6IWZmkNwBbAwJ+bfs3lSPFIJpa8OvZfra5/iqwHGU35zm2N6kYLyKitSS9HDgCWNL2NpJWBTacclEl2knSL4HTbJ/WXH8LmGsk9kLpJ5Jusz22ebw2ZbPqPravkXSj7fXqJoyY9Ulak1K5bFfKSfazbH+zbqrolaQxdB2Csj3iqs3lBFiLNLun9wUWppw8eQXwHWCrmrlicJJWprx2qzRDdwLfs/37eqmiB5cCbwX+DiBpB+D9wBspOySyANYHJI22/ULn2va3auaJ3klaFHgvZfK9+wPZPrUyxdA0C15Z9Oovs9l+VtJswA+Ap4GdbU9qNoZExEzUvPd9AlgNmKszbnvLaqGiVz8ATmZy2ebfA6cDWQDrDzsB50maBPw/4JEsfvUFS9oMWAb4IrCt7d9JmoP0no2YaSStBOzefP2b8n6H7S1q5oreSdoP+DzlFNgkyqZVAyvUzFVDFsDaZX9gPeAGANv3SFqsbqQYjKQNKWUOv9d8CViTUtt/R9vX18wXA5rbdmfxa1/KRPxWtv8l6Ut1o8UQ3CPpLOBk23fUDhND8nPgKuBi4IVB/m20jKTHKR+guz0KjAM+Zvu+4U8VPbha0iWU0iXzAZs1i1+bUxbDImLmOo0yifRmSjmhvYB/VU0UvVrE9s8kHQxge6KkfH5pOUkLd12+BzgXuAb4vKSFR+JO+D6zH2Xh6zngPOAjkq4EdmuuI2LmuItyr/5m2/cCSPpI3UgxRAcBr7H979pBassCWLs8a/s5SQA0O3NTo7L9/g/Y3fblXWPnSroUOJTSpDXa6SFJhwJLAzsAr7T9H0lLkJ4M/WQs8DbgBEmjgJOAn9p+rG6s6ME8tj9ZO0RMt68Df6GUohHl93BF4BbK7+HrqiWLabK9n6RNKBNJ/wDOlLRI8/RO9ZJFjBgvs32ipANtXwFcIemm2qGiJ09KehnNPbqkDSgbP6LdbubF8yoC3tR8jcid8P3E9g3A6zvXkranVGw5h5y+jJiZdqTc310m6ULgp5S/n9E//gA8VTtEG6QHWItIOgp4BHgncADwAeAO24cM+INRlaTf215pGs/dbXvl4c4UvWluYN9PmQT8A/Ap4HZgC+AQ2z+uGC+mQ3OC4cfAgsCZwOGd3UrRPpK+AFxr+5e1s8TQdfdk6Bq71fYaU3su2kvSorZzAiViGEi63vYGkn4NfAN4EDjT9oqVo8UgJK0FHAe8BvgtsCilhOyEqsFiUM0muQ1tX1M7S0REP5E0L/AWSinELYFTKX2DL6oaLAbV9G47mVJp7tnOuO0PVQtVSRbAWqT5UPZuXtxM/vt1U8VgJN1se+1pPHeL7bWGO1NMH0lLAhsDE2zfXTtP9EbSaMoOzr0pvaR+SCkvtClwxLQWqKO+poTevJRF6OebYdseUy9V9ErSdcDXKIvNADsDH20mdm+1vUa9dBER7STpzZSSQktTFlPGAJ+3nVJefaCp0rIy5X79btvPD/Ij0RKSxttes3aOiIh+JWkhYBdgN9tb1c4TA5N0I3A1ZaP/pM647VOqhaokC2AtJmlr4OO231A7S0ybpH9SjgL/z1PArrZfPsyRIkYUSfcBlwEn2r52iue+MRJ3t0QMB0krAMcCG1JKCF0PfAT4K7C27asrxouIiJghJO040PO2zx6uLDH9JH0FuA4425kIi4iIWVw2fkyWBbAWkLQl8B1gSUpD1i9TjigK+GI+ULebpL0Gen4krqxHDCdJm0w50S5p45Q46Q9NHf/NmsvLbV9QM09ERMTMIOkbAz2fDTvtJenkAZ627X2GLUxMt67KAy8AT1PmW1J5oM9Imsd2etpERAxC0hHAn4DzeXEJxIdrZaolC2AtIGk8Zcf0dcA2wI+AT9n+ZtVgERF9YGqlRlN+tD9I+hKwLqVkJZS64uNsH1wvVfRK0lyU0s2rAXN1xjMR2B8kbQzcavtJSXsCawHH2r6/crSIWZKk5yh9o35G6fv1okby2TQXETFtkjYCTgDms72MpLHAfrY/UDlaREQrSfrjVIZte4VhD1NZFsBaYMqJWkl32165ZqaIkaRp6vm07UmSVgJWAX6Vmv7tJmlDYCPgw5Q+RB1jgB1sj60SLHomaQKwhu1JzfVoYLzt1esmi15IOgO4C3g7cBiwB3Cn7QOrBoueNL9/Y4HVgR9QJpV2tb15zVwRsypJL6PpmwFMBE4HzrT9SNVg0bPmNTwU2IRS+vdq4DDbD1UNFj1L5YH+JekGSr/Z8zolvST91vZr6iaLiGgfSaOAXWyfXjtLG4yqHSAAWFDSjp0vYLYpriNi5roSmEvSUsBFwDsok4HRbnMA8wGzAfN3fT1GuTmK/rBg1+MFqqWI6fFK258FnmxOLrwJWL9ypujdxKYHyluAb9r+FuVvaETMBLYfsv0d21sAe1Pe/+6Q9I7K0aJ3PwX+BexE+az5L8pCZvSBpvLAgcAdzdeBko6smyqGwvYDUwy9UCVIRETLNZuMP147R1vMVjtAAHAFsF3X9ZVd1wbSAyxi5pLtpyS9Gzje9lGSbq0dKgZm+wrgCkk/SMmuvnUkMF7SZZRSUJsBn6obKYagc0r2EUmvAf4OLFYxTwzN45IOBvYENmt2Cc5eOVPELE/SWpSSv28AfgXcXDdRDMEStg/vuv6CpN2qpYmh2pYXVx44BRgPpPR2f3igKYNoSbNTFjPvrJwpIqLNLpZ0EGWzzpOdwZHYAywLYC1ge+/aGWLGaUpC/QT4BfBj2ztVjhSDU1NObw9KPxuA0RXzxND8QNL/1PO1vWWNMNE72z+RdDmlDxjAJ23/vWKkGJrvSVoI+AxwHuVE5mfrRooh2I1SvvLdtv8uaRng6MqZImZZkg6jnJS9k3KS6GDbE+umiiG6SNLbKH3coJwC+3XFPDF0CwKdib9UHugv7wOOBZYC/kqp3LJ/1UQREe3W2aTT/bfSQHqARcRLI2kdYC/Kzs7v2j6kcqQYhKTNgY8B19j+sqQVgA/b/lDlaNEDSWt3Xc5FKUsz0fYnKkWKQUhaxfZdzS74/2H7luHOFEPTnBba2fbPBv3HERGBpEnAH4GnmqHOjbgoDcnT/7KlJD1Oeb0EzAtMap4aBTxhe0ytbNE7SbsDXwJeVHkg/VHar+kTfKrtPWpniYiI/pMFsIiXSNLhwAmdEmxNc+RfAvcAf7d9UM18ESORpBttr1c7R0ydpO/Z3rcpfTgl5/Ref5A0zvY6tXPE9Gn6zH6ZUrZSTJ6Ez0RuxEwgadmBnk8554iZT9ISTK48cGMqD/QPSVcDW9p+rnaWiIh+0bQqWJWyWRwA26fWS1RHFsBaotlJvYFPEhqSAAAgAElEQVTta2tniaGRNKGzY7O5sT0fOMz2mZJusr3uwP8LUZuk3wC72H6kuV4I+KntN9ZNFr2QtHDX5ShgbeAbtleuFCl6JGku288MNhbt1DST/zepKd6XJN0LbGc7/TMiInrU3Ce8ihdPJF1ZL1H0StKPKP3Xr7J9V+08MTSSTgVeTSm73f2586vVQkVEtJikQ4HXURbAfglsA1xte+eauWpID7CWsD1J0reANWtniSEb3fTNWAY4EXi/7UslCZinbrTo0aKdxS8A2/+R9PKagWJIbmZyWZqJlPJC7x7wJ6ItrgWmLIM4tbFop9QU72//yOJXRETvJL0HOBB4BXArsAFwHZCT6/3hRGBT4DhJKwLjgSttH1s3VvToD83XKGD+ylkiIvrBzsBYYLztvZt5zh9VzlRFFsDa5RJJOwFnO0fz+smngEuB54DbgY0kTQT2pNwQRfu9IGkZ23+G/57kmzTIz0RL2F6+doYYGkmLUxpYzy1pTcriJcAYsnGgb+R3r++Nk3Q6cC7wbGfQ9tn1IkVEtNqBlPJ519veQtIqwBGVM0WPbF8m6UrKa7gF8D5gNSALYH3A9udrZ4iI6DNPNwduJkoaA/wTWLp2qBqyANYu+wEfpUzGP016MfQF2+dTyh7SnPo6APgkZUfZFytGi94dAlwt6QrK792mwL51I8VQpK5x33kj8C7KDurusiWPA5+uESiGTtI8lM8tyzQ93V4FrGz7gsrRojdjgKeArbvGDGQBLGImknTglCdOpjYWrfSM7WckIWlO23dJSsntPiHpEmBeyibVq4B1bf+zbqrolaRFgU9QFi277/lyAjMiYurGSVoQ+D6lctITjNCDGukBFhEBSFqEUsYEyq7Of9fME71LXeP+JWkn22fVzhHTpzk9dDPwTtuvaRbErrW9RuVoERGtJekW22tNMTbedkrht5ykc4C9gQ9Tyh7+B5jd9rZVg0VPJH2N0iv4WeAa4ErgOttPVw0WPZF0EaXv7EGU03t7Af+y/cmqwSIi+oCk5YAxtidUjlJFFsBapDk9tAewvO3DJS0NLGH7xsrRImZJklZpdm5Otd+Q7VuGO1MMnaTbmVzXeGynrrHtN1SOFj2Q9Cb+dyfnYfUSRa8kjbO9TvfEraTbbI+tnS0GJ+kVwHHAxs3QVcCBtv9SL1XErEvS7sDbgU0ov28d8wOTbG9VJVhMF0mbAwsAF9p+rnae6J2k+SmVCA4CFrc9Z91E0QtJN9teW9IE26s3YzfZXrd2toiItpK0FLAsXVUAbV9ZL1EdKYHYLsdT+g5tCRxOOZr4LUqN6oiY8T5KKXV4zFSeM2lo3S9S17hPSfoOpefXFsAJlCat2fTRP56TNDfl7yVNQ/lnB/6RaJGTgR8DuzTXezZj2TwQMXNcC/wNWIQXf/Z8HBiRu3H7haR1gUVs/6ozZvsKSdsCr6Wcho6Wk3QAZQF6beBPwEm8eDE62u355vvfmg10DwILV8wTEdFqkr4M7AbcAbzQDJtyAnpEyQmwFumUw8hO6ojhJWku288MNhbtJOl4St+otwEfo2weuNX23lWDxaA6Ozi7vs8H/Mr2prWzxeAkvQH4DKX86EWUk0Tvsn15zVzRG0m3TlmucmpjETHjSVoWeJXti5uNBLPZfrx2rpg6SZcCe9u+f4rxZYGT04OoP0g6iLLgdbPtibXzxNBIejPl9VuacoJ9DPB52+dVDRYR0VKS7gZWtz3iN6nmBFi7PC9pNJN3Ui9KOREWfaB5vd4LLMeLj5buUytT9OxaYMoyiFMbixay/YHm4XckXcgIrmvchzo9F56StCTwELBExTwxNDcDO1L6Jwo4kFLKK/rDQ5L2BH7SXO9O+R2MiJlI0nspFQgWBlYEXgF8B0gJxPaaf8rFLwDb9zd9hKM/jLX9le4BST+0/Y5agWJILm42qD5KqR4REREDuw+YnVRpyQJYy3wDOAdYTNIXKaWgPlM3UgzBzyk7ki5m8tHSaDFJiwNLAXNLWpMygQtlN9k81YLFkHT1T1zB9mGSlpG0Xvon9oULJC0IHA3cQtkAckLdSDEE5wPb2P4FgKRXA2cAr6maKnq1D2UH9dcov3vXAjk5GzHz7Q+sB9wAYPseSYvVjRSDWGiA53LP0D9W676QNBulHGL0h99K+gdlzuUq4Grbj1bOFBHROpKOo9zfPQXcKukSuhbBbH+oVrZaUgKxZSStQtn9J+AS23dWjhQ9Stmg/iNpL0oD5HWAcV1PPQacYvvsGrliaCR9m6Z/ou1XS1oIuCgNkfuLpDmBuXIj2z+a/gufALYFVgFOBfawfWvVYBERLSbpBtvrd8reN5Pwt9hevXa2mLqmZ+lDwGfcTKA0G7A+Dyxue9+a+WJgkg6mlEufmzIZ2Nn0+BzwPdsH18oWQyNpGWBTStntbYFHMgcTEfFizVznNNk+ZbiytEUWwFqmKYH4cl5cQu/P9RJFryR9AbjW9i9rZ4mhkbST7bNq54jpk/6J/UvS/sBpth9prhcCdrd9fN1k0StJb6Usgs0P7GT795UjxSAkfcL2UV07A19kJO4IjBhOko4CHgHeCRwAfAC4w/YhVYPFNEmal3JCfT2gs8ljLGUD3XtsP1ErW/RO0pFZ7Opfkl5BWfzanPL79zDlFNiRVYNFRLSQpDWAVwK/y+GaLIC1iqQDgEOBf1BK6AlwdgP2B0mPA/NSdpI93wzb9ph6qaIXku4DzgJOyhtD/5F0A7ARcFOzELYo5QTYmpWjxSCmdnK2eyEz2mkqCydbAX8A/gRZQGk7SdvZPn9aOwNH4o7AiOEkaRTwbmBryv3er4ETnBvz1pO0ApPL6P3O9n0188TQNL97bweWt324pKWBJVI2vT9ImgTcBBxh++e180REtJWkzwLvoPTsXh840vb366aqKwtgLSLpXmB922lAHjGMJM0PvI3S+2QUcBLwU9uPVQ0WPZG0B7AbsBZwCk3/RNtnVA0Wg5J0O7B6Vzmh0cAE26sN/JNRU0oqzBok7TLl38mpjUXEzCNpYeAVtifUzhIxq0vZ9P4maSywCbAZsAxwD3CF7ROrBouIaBlJvwPWtf2UpJcBF47097osgLWIpMuAN9ieWDtLTB9J21M+kAFcbvuCmnli6CRtDvwYWBA4Ezjc9r11U8Vg0j+xP0k6GlgW+G4ztB/wgO2P1UsVQyFpDmCl5vJu288P9O+jPTrlYwcbi4gZS9LlwPaUkvc3A/+klFH/SM1cEbO6lE3vf5LmoyyCbQrsCWB72aqhIiJaZsp7Okk32167ZqbaZhv8n8TMJumjzcP7gMsl/QJ4tvO87a9WCRZDIulLwLrAac3QgZI2Tp3x9mtOnbyJcgJsOeAYyuu4KfBLJk/uRkvZvkvSn4FVgX/XzhM9+yRl0ev9zfVvKD02og9Ieh3l1OWfKIvPS0vay/aVNXPFwCRtQ2kcv5Skb3Q9NQbIJqyImW8B249Jeg9wqu1DJeUEWMTM93xz39epPLAo5URY9AFJ44A5gWuBq4DNbN9fN1VERCutIOm85rGAFbuusb19nVj1ZAGsHeZvvv+5+Zqj+YKpNCeP1toWWMP2JABJpwDjgSyAtd89wGXA0bav7Ro/U9Jm0/iZqKw5cfkNSgPkzwDfovRQXE7SJ1OGrf2av5ffbr6i/xwDbG37bgBJKwE/AUb07rI+8CAwjnIC5eau8ceBnECJmPlmk7QEsCtwSO0wMXSSVrV9R/N4A9vX184UPfkGcA6wmKQv0pRNrxsphmAb2/+qHSIiog+8ZYrrr1RJ0SIpgdgi6cXQ35qdm6+z/XBzvTClDOLqdZPFYCTNZ/uJ2jliaCTdBuwCLEBZwFzd9n2SFqOUQXxt1YAxTZJ+ZnvXpgfY/3wQyd/N/iBpwpSv1dTGop0kjQGetP1Ccz0amNP2U3WTRczaJO0CfBa42vYHJK1A2YS1U+Vo0SNJFwALAT8H3mM71SL6RMqm95+uiklTlYpJERExmCyAtUh6MfQ3SbsDX6JMxIvSC+xTtk+vGiymSdJxDHDK0vaHhjFODNEU9ftv717w6n4u2kfSErb/JmmqNftTzqQ/SDoZeAH4UTO0BzDa9j71UkWvJF0PvL6zAaTpq3GR7Y3qJouIaBdJywEP236sa+wAyo7qt9s+q1K0iFmepEObhytTWk50ynhtB9xoe88qwSIiom+kBGILpBfDrMH2T5qm1us2Q5+0/feKkWJw42oHiJdklKSFgFHApOaxOs/VixWDsf235nsWuvrb+4D9gc5mgauA4+vFiSGaq/v0s+0nJM1TM1DESCDpKOALwNPAhcDqwEds/2jAH4yazgK27FxI+hCwG7AGpQR3FsBaTNLjTN70qK7HswFz2M68WIvZ/jyApCuBtWw/3lx/DvhFxWgREdEn8kbfDg9SejCkF0MfkrSK7bskdU7q/aX5vqSkJW3fUitbDCw9ovreApS/mZ1Fr+7ftRxv7gOSdgS+DCxGeR0F2PaYqsFiUE25vNtsrwKk9Ex/elLSWp3PKZLWpkzIR8TMtbXtT0jaAfgTsCNwJZNP00b7zGH7UQBJRwBrAm+w/ZSkBepGi8HYnr/7ujnxvD+wH6UnWPSHlwPPdV0/14xFRESPJM1l+5naOYZbFsBawPZtwG2SzgSWa4bvHYn/QfapjwL7AsdM5TnTtVsw2knSosAngVWBuTrjtvPatZjt5WpniJfsKGC79F/oP7ZfkHS3pGVs/7l2npguHwbOkPQgZfF5ccqJhoiYuTr34G8CzrD9qKSB/n3Ud29T9vcVlMWvlZvFr1dXzhVDIGlBynvfO4EfA+vafqhuqhiCU4EbJXUWLd8K/KBenIiI/iDpJuAnzdeZwMZ1Ew2/LIC1gKTZgCOAvYE/UyYhlm4+ZB9i+/ma+WJgtvdtHm4z5aKlpLmm8iPRPqcBp1MmIt4H7AX8q2qiiJHhH1n86msLAb+TdCPwZGfQ9vb1IkWvbN8kaRVKTw2Au/OZM2JYXCDpLsqJy/c3G7Gy8bHd3gbsQjlxch9wuaR/AatQ7huixSQtAnyMssnjJGDNzom+6A8quwROBX4FbNoM7217fL1UERF9Yxvgg8D9wEGVs1QhO1WiapP0NWB+Su33Tj3jMZSmuk/bPrBmvuiNpFtsrzXYWLSPpJttry1pgu3Vm7GbbK872M9GxPSTdCzl1Mm5wLOdcdtnVwsVPZO0+dTGbV8x3Fli6CS9c2rjtk8d7iwRI42khYFHm9O08wAvs/1A7VzRm2aT42uBe2w/UjtPDEzSk5TNjSdT2ky8iO2Ucu4Dkm63/draOSIi2q45UPO5Ts91SSsC51HK/i5u+z0189WQE2Dt8GZgJXetRtp+TNL7gbuALIC1mKTFgaWAuSWtyeR+RGOANJPvD50d73+T9CZKX76FK+aJGCnGAE8BW3eNGcgCWB/oXuhqdlc/5Oys6ifdmzzmArai9FLMAljETGb7YRVbAW+n3A+ml02faKp+3FQ7R/TsaCb3B55/oH8YrXaLpHVt53cvImJga3Utfq1NKfu7j+1rmuotI04WwNrBU5swanYEZiKp/d4IvItSE/4YJi+APQZ8ulKmGJovNA2sPwYcR5mU/0jdSBGzPtt7184QQydpA+BLwMPA4cAPgUWAUZLeafvCmvmiN7YP6L5ueqP8tFKciBGj+Rv6dkr/moWB/Rmh5WgihoPtz9XOEDPE+sAeku6nlN4WZS5t9bqxIiJax5I2A5YBvghsa/t3kuZghG4ESQnEFpB0LnD2lCVnJO0J7JpeGv1B0k62z6qdI2Kkk9TpKfUt29+sGiYG1BzNn9oGkH0qxIkeSRpH2eCxAPA9Sg/M65t+Uj+xvWbVgDFdJM0O/Nb2yoP+44gYMklHUPpI/ZnShPwcYJzt5asGi4joA5KWndp455RDREQUktanLHw9B/wBmBu4ktIL87e2P1kxXhU5AdYO+wNnS9oHuLkZW4fyH+gO1VLFUK0t6ZJOHXhJCwEfs/2ZyrliEE3z8fcCy9H1dzGT8P3J9qubcmzr184Sg7qg6/FclPe8Bytlid7NZvsiAEmH2b4ewPZdpUd59ANJ5zN5AXoUsCpwRr1EEbO89wC/B74NnG/72VT76C+Sfmj7HYONRcSM11XOazHKfUNEREyF7RuA13euJW1PqV52DnBirVw15QRYi0jaElitubzD9iU188TQSBo/5a53SbfYXqtWpuiNpGuBqygL0C90xnOiL2J4SRoFXG17o9pZYtq639umfJ/L+17/kLR51+VE4H7bf6mVJ2JWJ2k08AZgd0rPvcsokxNL255YM1v0ZirveaOB222vWjFWxIjQTOAeAywJ/BNYFrjT9moD/mBERIx4OQHWIrYvBS6tnSOm22hJc9p+FkDS3MCclTNFb+YZiUeAZxWSdgS+DCxGqQXfqQc/pmqwmB6voryO0W5jJT1G+V2bu3lMc50duX3C9hXd15I2kXSw7f1rZYqYldl+AbgQuFDSnMCbKRU//tpUkXh71YAxTZIOppT+nfI97zlKKeDoI5KOAk63fbOkr9lO7+f+cDiwAXCx7TUlbQHsWTlTRET0gSyARcw4pwGXND1tAPYGTqmYJ3p3gaRtbf+ydpCYLkcB29m+c9B/Ga0i6XFKCTY13/8OZDG65WyPrp0hZgxJawJvp/Ql+iNwdt1EESNDs2HuLOAsSWOAt1aOFAOwfSRwpKQjbR9cO0+8ZDcCH5e0GnBT7TDRs+dtPyRplKRRti+T9PXaoSIiov1SAjFiBpK0DaWkCcBvbP+6Zp7oTTMJPy/wLPA8OUHUVyRdY3vj2jkiIvqBpJUoJdh2B/4NnA4cZHuqzeUjIqKQtDFwq+0nJe0JrAUc2+lNFO0k6X3AL2w/0FzPTel5OT9wYbPAGS0n6WLKZoEjgUUoZRDXTen0iIgYTBbAIiKiLzWlDwE2BxYHzqUsYgJgOycZ+oCk1YHl6DqVntcuYuaRNInS9/Ldtu9txu6zvULdZBER7SZpAjAWWB34AXACsKvtzQf6uahL0u22X9s8Xgg4DzgH+Bpwg+31auaL3kiaF3iGsll1D2AB4DTbD1UNFhHRUs3Gx49TeiZ2z7dsWS1UJSmBGDGDdJXyApgDmB14MqeI+oukFWl2xqehbutt1/X4KWDrrmuTUl6tJ+kkyiTS74BJzXBeu4iZa0fgbcBlki4EfkqZTIqIiIFNtG1JbwG+aftESe+uHSoGNXuzeLIIZcPcMbZ/BCBpnqrJome2n+y6TKuJiIjBnQF8B/g+8ELlLFVlASxiBrE9f+exJAFvoTRpjZaTtCRlMnB34LWUsgpvqxoqBmV7byjlaGxf0/1cU6Im2m8D26vWDhExktg+Fzi3mQx8C/BhYDFJ3wbOsX1R1YARI4SkDwA/t/1XSR+2nV427fe4pIOBPYHNJI2ibHqMdjsGuA8YDUwAkLQMsBdwd8Vc0YMpNhq/6CnStiAiYiATbX+7dog2SAnEiJlI0njba9bOEVMnaV/KotdSwM+ar5/bXr5qsBgSSbfYXmuwsWgfSSdSduHeUTtLxEjWlITaBdjN9laD/fuIeOkkHQRsQynfPNr2GytHikFIWhx4O3CT7auaRZTX2T61crQYhKTRzcPZKJsd3wjcAnzE9r+rBYshyfxKRMTgJC3cPPwQpV/iOby4XcjDNXLVlAWwiBmkqx8RwChgHWBz2xtWihSDkPQccB3wMdvjmrH0QekTkjYENqKcXvha11NjgB1sj60SLHomaXNKH4a/Uz6QdXZyrl41WERExAzWlM27yfaDzbWAbwNbAUfb/l7NfBERbZdNjhERg5P0R8rJ2amVufdInPNMCcSIGae7H9FE4E+U0kLRXktQdrwf0+zo/BkpY9JP5gDmo7yXzd81/hiwc5VEMVQnAu8AbmdyD7CIiIhZ0eHAGgCSZgd+AvyVUn77ciALYC0naQPgOODVlM+ho4EnbC9QNVhEREREI1Wt/ldOgEVEAJJeAexGKYk4L6UPyqfrpopeSFrW9v21c8TQSboup2QjImIkkHQnsBpl087ZwKW2v9g8d1tOrrefpHGUPsFnUKp9vBNYyfbBVYNFzMKmqLTzFeCg7udtnz28iSIi+oOk/YHTbD/SXC8E7G77+LrJhl8WwCJeIknHMfWmrADY/tAwxokZQNJKwNtsH1Y7S0ybpPMZ+Hdv+2GME9NB0vHAgsD5vLgmdW5kIyJiliLpc5T+UfMD/wA+CFxDOQn9DttvqJcueiFpnO11JE3olGtOT6KImUvSyQM8bdv7DFuYiIg+IulW22tMMTYiP7ekBGLESzeu+b4xsCpwenO9C3BHlUTxktj+PZDFr/b7Su0A8ZLNTVn42rprzJSd8REREbMM25+TdAKlVPqzlDLAvwRuo5wkivZ7StIcwK2SjgL+Run9HH1A0suBI4AlbW8jaVVgQ9snVo4WA7C9d+0MERF9arQkuTn9JGk0pYTziJMTYBEziKTrgU1sT2yuZweusr1B3WQRERERERHxUkhalnJ6bw7gI8ACwPG2760aLHoi6VfAycAhtsdKmg0Yb/u1laNFRETMcJKOBpYFvtsM7Qc8YPtj9VLVkQWwiBlE0t2UHWQPN9cLAdfbXrlusohZm6RXAUdSTmDO1Rm3vUK1UNETScsDBwDL0XUqPeUrIyIiom0kzQs8bXtScz0amNP2U3WTRS8k3WR73e7yT1MrDxURETErkDSKsui1VTP0G+AE2y/US1VHSiBGzDhfAsZLugwQsBnwuaqJYkCS1hroedu3DFeWeElOBg4FvgZsAexNytH0i3MpJaDOByZVzhIRERExkEuA1wNPNNdzAxcBG1VLFEPxpKSX0fQQlrQB8GjdSNGLZhJ3A9vX1s4SEdEvbE+S9APgUtt3185TU06ARcxAkhYH1qd8qL7R9t8rR4oBNIuV02LbWw5bmJhukm62vbak2zslTDpjtbPFwCTdYHv92jkiIiIiBjONZvI5QdQnms2PxwGvAX4LLArsbHtC1WDRk+6TexERMThJ2wNHA3PYXl7SGsBhI7HiTk6ARcxY6wGbNo9NOdUQLWV7i9oZYoZ4ttkVeI+kDwJ/BearnCl6c6ykQym7p5/tDOb0ZUREzKok7TiV4UeB223/c7jzxJA8KWmtzucUSWsDT1fOFD1oylVu3nytTKnYcrft56sGi6G4RNJOwNnOTv6IiF4cSpmnvhzA9q1NG4oRJyfAImYQSV8C1gVOa4Z2B26y/el6qaJXkl7D//aQOrVeouiVpHWBO4EFgcOBMcDRtq+vGiwGJelI4B3AH5hcAjGnLyMiYpYl6RfAhkCnEsHrgJuB5Sm7cn9YKVoMovnM+VPgQcoCyuLAbrZvrhoseiLpRtvr1c4R00fS48C8wAuUhWdR7hvGVA0WEdFSkq63vcEUvS8n2F69drbhlgWwiBlE0gRgjSmaIo8fiX9Y+k1zAuV1lAWwXwLbAFfb3rlmrhgaSfOkCXl/kXQvsKrt52pniYiIGA6Sfg280/Y/muuXA6dSNs9dafs1NfPFwCTNTjlBBDlB1FckfQ2YHTgdeLIznsoDERExK5J0IqV/6aeAnYAPAbPbfl/VYBWkBGLEjLUg8HDzeIGaQWJIdgbGUhYs924mIn5UOVP0SNKGwImUsofLSBoL7Gf7A3WTRQ9+S/m7mZJPERExUizdWfxq/LMZe1hSFlNaaBplKwFWkoTts4c1UEyvTq+2w7rGDKTyQB+QJGAPYHnbh0taGljC9o2Vo0VEtNUBwCGUdhM/Bn5NqZo04mQBLGLGOQIYL+kyynH8zSir7NF+T9ueJGmipDE0ExG1Q0XPvg68ETgPwPZtkjarGyl6tCBwl6SbeHEPsBHXlDUiIkaMyyVdAJzRXO/UjM0LPFIvVgxgu+b7YsBGlN3UArYArgWyANYH0v+57x1PKZm+JWUC9wngW5Q2FBER8b/eZPsQyiIYAJJ2YfJn0BEjC2ARM4CkUZQPYxsw+QPYJ23/vV6qGIJxkhYEvk/pwfAEcF3dSDEUth8omwL/64VaWWJIDq0dICIiYpjtT1n02ri5PhU4y6U3QSboW8j23gCSLqKUbv5bc70E8IOK0WKIJL0JWI0X930+bNo/ES2yvu21JI0HsP0fSXPUDhUR0WIH87+LXVMbm+VlASxiBmhOD33C9s9oTqFE/+gqlfcdSRcCY2xPqJkphuQBSRsBbvoyHAjcWTlT9MD2FU3J0c7GgRttpxxiRETMspqFrjObr+gvS3cWvxr/AJapFSaGRtJ3gHkoC80nUMrgp3xe/3i+6bNuAEmLUjYhR0REF0nbANsCS0n6RtdTY4CJdVLVNap2gIhZyMWSDpK0tKSFO1+1Q8W0SVql+b5W5wtYGJiteRz94X2U3dRLAX+l1Pffv2qi6ImkXSkTD7sAuwI3SNq5bqqIiIiZR9KOku6R9KikxyQ9Lumx2rmiJ5dI+rWkd0l6F/AL4OLKmaJ3G9l+J/Af258HNgRWqpwpevcN4BxgMUlfBK4GjqwbKSKilR4ExgHPUKpcdb7Oo7QPGXFUNqBFxEsl6Y9TGbbtFYY9TPRE0vds79v0bZuSbachcsRMJOk24A2dU1/NTs6LbY+tmywiImLmkHQvsJ3tnFbvQ5J2BDZtLq+0fU7NPNE7STfYXl/S9cCOwEPA72y/snK06FGzgXUrSg++S/J3NCJi2poKSWLyZo+7bT9fMVI1KYEYMYPYXr52hhga2/s239NvoQ9JOo6mBMbU2P7QMMaJ6TNqipKHD5HT6RERMWv7RyZt+5fts4Gza+eI6XJB0/f5aOAWyn3E9+tGil5J+qHtdwB3TWUsIiL+10aUXrN/oiyELS1pL9tXVk1VQRbAIl4iSesD3wNWBG4H9slNbX+RtD9wmu1HmuuFgN1tH183WQxiXNfjzwOH1goS0+1CSb8GftJc7wb8qmKeiIiImW2cpNOBc4FnO4PNwkq0mNcbC4QAABxeSURBVKQNgOOAVwNzAKOBJ22PqRosemL78ObhWZIuAOay/WjNTDEkq3VfNP3A1q6U5f+3d+9R2pd1vcffnwdEQAVU8LQ3AeIWQjmIkCKYiVGah9I0M0xNM7e5IjR3bQ+5KV1pmuxMd6aiLM8HNBU1yMM2RTwgAgIGmKLksVLcgCAgD5/9x31PDo/PYR6embnumXm/1po1v+v63fc8n2fNmlnz+31/1/eSpJXgROCX2l4CkOTuTO69rLnfnbZAlLZRkrOB5wCfBB4B/G7bNdlTdaVKcl7bQzaYO7ftvUZl0tbx+7VyTVsJHTUdnmErIUnSapbk5I1Mt+2Tlz2Mtsr0uu83gVOAw4AnAHdv+5yhwbQgST4FfAI4Aziz7VWDI2kBkjwHeC6wE3ANk1UMANcDr/XnT5I2Lsn5bQ/a0txaYAFM2kZJzml76KbGmn1JLgAO6vQX4vRpsvPb3mPz79Ss8OduZUqyD/CdttdOxzsBd2z79aHBJEmSNpDk7LaHzb955ENYK8f07877Tz/uy2QF5hltnzk0mBYkyYstdknSwiV5A3Aj8Jbp1LHAdmvxoStbIErbbrfpCoaNjm1nsiKcDrwzyWum46dN5yQtrVOY9KWes346d/iYOJIkLY0kf9z2pZvaw9S9S1eEa5LsAJyX5KXAd3Dv0hWj7deSXMtk5dD1wAOZtLPUynBakp/fcHIt7mUjSQv0dOAZwNzfmGcAa3KrF1eASdtoE21M5tjOZAVIso5J0etB06mPACe1XT8ulbYkyVX85AbSzkxaYsCkLUbdj2H2baL96BfbHjwqkyRJSyHJw9t+IMkTN3a+7RuXO5O2TpK9gH9jsv/XM4Fdgb9t+5WhwbQgSb4KfA94G5ObgOe1vXFsKi1Ukg/MG+4I/BzwhbZHD4okSVohLIBJkqQhknwEeGXbU6fjXwWOa/ugzb9TkqSVKck+bb+2wdzhbT8/KpMWbroC7O7T4SVtfzwyjxYuyR8y2Xd2T+BiJvuBfbLtV4cG082SZE/gr9v++ugskjRLkryr7W9Mt3vZWNcB9wCTpLUmyZHACcBeTFrDzq0guuvIXNJql2Rf4K3Af2Hyh9k3gSf4JLUkabVK8gXgEW2/NR0/AHhV2wPHJtOWJPkF4I3A15lcL+wJPNEWbCtLklsDvwM8G/ivbbcbHEk3Q5IAX2p7wOgskjRLkty57XemK9d/StvLljvTaBbAJK15SS5m0sbkC0z2IAKg7feHhZLWkOmNCNr+cHQWSZKWUpLDmey/8HDgUODFwMPafmNoMG3RtHj5W20vmY7vDry97b3HJtNCJHk5kxVgtwY+DXwKOKPtpUODaUE22D9xHXAI8PW2jx+XSpJWhiS7A9/vGi0EbT86gCTNgCvanjY6hLTWJLkj8BfAXdo+JMkBwBFtXz84miRJS6Lt55McB3wYuBb4xbb/MTiWFuYWc8UvgLZfTnKLkYG0VT4DvLTtv40Oopvl7HnHNzApPp85Kowkzaok9wVeAlwOvBB4M7A7sC7JE9qePjLfCK4AkxZJkp2BPwJ+pu1Tk/w3YL+2HxwcTVuQ5CXAdsDfA9fNzbc9Z1goaQ1IchpwMvC8tgcn2R441zZQkqTVJskHuOk+DAcA3wF+AND2ESNyaeGSnMykW8RbplPHAtu1ffK4VFqoJB/bcJ/Zjc1pdk334Nufye/SS9pePziSJM2cJGcDzwV2BV4LPKTtZ5Psz+ThgXsNDTiAK8CkxXMykxZ6R0zH3wJOASyAzb77TD8fNm+uwNEDskhrye5t35XkOQBtb0iyfktvkiRpBfqr0QG0zf478AzguOn4DCbtLDXDkuwI7AzsnuS2TPZvA9iFyT60WgGS/ArwGuCrTL6H+yR5mp1cJOmnbN/2wwBJ/rztZwHaXjzZPnHtsQAmLZ592z42yeMA2l6TtfqbZYVp+8DRGaQ16uokt2f6RPx0qf4VYyNJkrT42n5idAbdfEm2A77Ydn/gxNF5tFWeBhwP3IXJA6tz1+hXAq8aFUpb7UTggW2/ApBkX+BDgAUwSbqpG+cd/2iDc2uyFaAFMGnxXJ9kJ35yI3df5rXT02xL8lDgHsCOc3Nt/3xcImlNeBZwKrBvkjOBPYBHj40kSdLSSfIo4C+BOzC5ER+gbXcZGkyb1XZ9kkuS/Ezbfx2dR1un7T5JXuD13Yp21Vzxa+pS4KpRYSRphh2c5Eomf2PuND1mOt5x029bvdwDTFokSY4Bns+kn/+HgSOBJ7X9p5G5tGVJ/o5JW4wHAicxuQF/VtunDA0mrQHTfb/2Y/LH2CVtfzw4kiRJSybJV4CHt71odBZtnSSfBO4FnAVcPTfv/m2zLcl5bQ9Jck7bQ0fn0c2T5NXAXsC7mDx0/BjgX4GPArT9+3HpJEmzzAKYtAiSrGNSNPkYcF8mN3I/2/Z7Q4NpQZKc3/ageZ9vDZzW9v6js0mr0fTp903yAlaStFolObPtkaNzaOslecDG5m1vOduSvJ3JXs93YbJ/1H+eYrL68qAhwbRVkpy8mdNt++RlCyNJWlEsgEmLJMnZbQ8bnUNbL8nn2t4nyWeBRwHfB77U9m6Do0mrkhewkqS1KskrgDsB72Neu3Qf/lhZkuwOfL/eUFkRktwJ+Efgp1brtb1s+RNJkqTl4h5g0uL5aJJnA+/kpi0xLh8XSQv0wSS7AS8DzmHSUuGksZGk1avt74zOIEnSILsA1wC/NG+ugAWwGZXkvsBLgMuBFwJvBnYH1iV5QtvTR+bTlrX9LpM9UXYA7j6dtvX2CpJkH+APgL2Zdy/TFqSSpC1xBZi0SJJ8bSPTbXvXZQ+jmy3JLYEd214xOou02iW5I/AXwF3aPiTJAcARbV8/OJokSRIw6fQBPBfYFXgt8JC2n02yP/D2tvcaGlALMm1h+Sbg60zaH+4JPLHtJ0fm0sIk+SLweuAC4Ma5eVuQSpK2xAKYJAFJ7sdPP032pmGBpDUgyWnAycDz2h6cZHvg3LYHDo4mSdKSSLIj8BTgHsCOc/O2/51dSc5re8j0+KK2Pzvv3LkWwFaGJF8AfqvtJdPx3ZkUMO89NpkWYm7bgtE5JEkrjy0QpUWS5Akbm7eIMvuSvBnYFzgPWD+dLpMnBCUtsiTbt70B2L3tu5I8B6DtDUnWb+HtkiStZG8GLgZ+Gfhz4FjgoqGJtCU3zjv+0QbnfKJ45bjFXPELoO2Xk9xiZCBtlVck+V/Ah7np/onnjIskSVoJLIBJi+fwecc7Ag9isp+URZTZdxhwgJtYS8vmLOBQ4Ookt2d682i6x4btRyVJq9nd2j4mya+2fWOStwFnjA6lzTo4yZVM2ubtND1mOt5x02/TjDk7yUnAW6bjY4GzB+bR1jkQ+G3gaH5SlO50LEnSJlkAkxZJ2z+YP06yG/COQXG0dS4E7gR8Z3QQaY3I9POzgFOBfZOcCewBPHpYKkmSlt6Pp5//X5J7At8F7jAwj7ag7XajM2hRPB14BnDcdHwG8Lfj4mgrPQa4a9vrRweRJK0s7gEmLZFpO4UL2+43Oos2L8nHgUOYrEqZ307hEcNCSatYkm8CJ06H64BbMimKXQesb3vipt4rSdJKluR3gfcABzHZB/PWwJ+2fc3QYNIakWQHJnvwfavtv4/Oo4VJ8j7g9/yeSZK2livApEWS5AP8pAf8OuAA4JRxibQVThgdQFpjtmNywy8bzO88IIskScum7UnTw08Adx2ZRVoLkvwd8Mq2X0qyK/AZJvs+3y7Js9u+fWxCLdBuwMVJPo8PrUqStoIrwKRFkuQB84Y3AJe1/eaoPJI0q5Kc0/bQ0TkkSVpO0+uFH7Q9P8lvAD8PfAV4ddvrNv9uSTdHki+1vcf0+HjgF9r+WpI7Aae1vdfYhFqIDe63/Ke2n1juLJKklcUVYNLi+ZW2fzJ/Islfbjin2ZHkU22PSnIVP1m9B5NVKW27y6Bo0mq34covSZJWtST/h0nbwx2TXMJkJfTpwJHAG4BjB8bTZky/d29re+boLLpZ5u8ZdQzTLi1tv5v4J+lK0fYTSe4IHD6dOst2iJKkhXAFmLRINraiIcn5bQ8alUmbl2SvtpeNziGtNUlu1/by0TkkSVouSf657QFJdgS+Bdyh7fpM7sCf3/bAwRG1CUn+EPhN4M7Au4C3tz13bCot1HS/55cz+bn7OLD/tPi1PZM9u/cfGlALMl01+zLgn5g8THd/4H+0fffIXJKk2ecKMGkbJXk68PvAvknOn3fqNsCnx6TSAr0XOBQgyXva/vrgPNKaYPFLkrQGXQvQ9tokl7VdPx03yY/HRtPmtH0F8IokezEphL0hyU7A25kUw748NKC25GnA3wB3Ao5v+93p/IOADw1Lpa31PODwuVVfSfYAPgpYAJMkbZYrwKRtNN1I97bAi4H/Oe/UVd7knW1Jzp3r+T7/WJIkSVpMSb4JnMhk5cIzp8dMx8e33XNUNm29JPdi0rryoLbbjc4jrXZJLpi/UjbJOuCLrp6VJG2JK8CkbdT2CuCKJK8ALm97FUCSXZLcp+3nxibUZnQTx5IkSdJieh2TDhEbHgOctPxxtLWmLfMewmQV2IOYtGI7YWAkaS05Pck/Mll5CfBY4LSBeSRJK4QrwKRFkuRc4NBOf6imTySdveG+YJodSdYDVzN58nYn4Jq5U0w60uwyKpskSZKk8ZIcAzwO+BXgLOAdwPvbXj00mLTGJHkUcNR0eEbb947MI0laGSyASYskyXltD9lg7vy2B43KJEmSJEm6+aYPzT0NeE/bH4zOI60lSe4G3LHtmRvMHwV8p+1XxySTJK0U60YHkFaRS5Mcl+QW048/BC4dHUqSJEmSdLOd3/Yki18rW5I7Jnl9ktOm4wOSPGV0Lm3RXwNXbmT+iuk5SZI2yxVg0iJJcgfgb4Cjmewn9TEmG1r/+9BgkiRJkmZCkn3afm1Lc5odSb4JnLip8203eU6zY1r4Ohl4XtuDp3u6ndv2wMHRtBlJPt/28E2cu8DvnyRpS7YfHUBaLaaFrt8cnUOSJEnSzHoPsOEewe8G7j0gixZmO+A2o0Nom+3e9l1JngPQ9oZpe0vNtt02c26nZUshSVqxLIBJ2yjJH7d9aZJXMln5dRNtjxsQS5IkSdKMSLI/cA9g1ySPmndqF2DHMam0QN9p+2ejQ2ibXZ3k9kyv2ZPcl0kbPc22s5M8te3r5k8m+V3gC4MySZJWEAtg0ra7aPr57KEpJEmSJM2q/YCHMVnN8PB581cBTx2SSAuV0QG0KJ4FnArsm+RMYA/g0WMjaQGOB96b5Fh+UvA6DNgBeOSwVJKkFcM9wKRFkOTXgLsBF7T9x9F5JEmSJM2eJEe0/czoHFq4JLdre/noHNp2032/9mNS1Lyk7Y8HR9ICJXkgcM/p8Ett/+/IPJKklcMCmLSNkvwtk3YmnwYeBHyg7QvHppIkSZI0a5LswWTF197M68jS9smjMklrQZLHAKe3vSrJ85nsxfeitucMjiZJkpaQBTBpGyW5EDi47fokOwNntHUTa0mSJEk3keTTwBlMWnmtn5tv+55hoaQ1IMn5bQ9KchTwQuCvgBe0vc/gaJIkaQm5B5i07a5vux6g7TVJ7BEvSZIkaWN2bvsno0NIa9BcwfmhwOvafijJi0YGkiRJS88VYNI2SnIN8JW5IbDvdBygbQ8alU2SJEnS7JjecP90238YnUVaS5J8EPgWcAyT9oc/As5qe/DQYJIkaUlZAJO2UZK9Nne+7WXLlUWSJEnS7EpyFXAr4Prpx9xDc7sMDSatctPtCh4MXND2X5LcGTiw7YcHR5MkSUvIApgkSZIkSZJWnSS7tL0yye02dr7t5cudSZIkLR8LYJIkSZIkLYPpfsHHAvu0fWGSPYE7tz1rcDRpVUrywbYPS/I1oExWXc5p27sOiiZJkpaBBTBJkiRJkpZBklcDNwJHt/3ZJLcFPtz28MHRpFVrWnjes+2/js4iSZKW17rRASRJkiRJWiPu0/YZwLUAbX8A7DA2krS6dfLk94dG55AkScvPApi0SJIcmeQjSb6c5NIkX0ty6ehckiRJkmbGj5Nsx6QVG0n2YLIiTNLSOieJKy0lSVpjbIEoLZIkFwPPBL4ArJ+bb/v9YaEkSZIkzYwkxwKPBQ4F3gg8Gnh+21OGBpNWuen1+t2Ay4CrmewF1rYHDQ0mSZKWlAUwaZEk+Vzb+4zOIUmSJGl2JdkfeBCTG/Afa3vR4EjSqpdkr43Nt71subNIkqTlYwFMWiRJXgJsB/w9cN3cfNtzhoWSJEmSNFOS3BbYE9h+bs5rBmnpJTkUOIpJC9Iz/bmTJGn1swAmLZIkH9/IdNsevexhJEmSJM2cJC8EngR8lek+YHjNIC25JC8AHsPkgVWAXwNOafuicakkSdJSswAmSZIkSdIySHIJcGDb60dnkdaS6c/ewW2vnY53As5ru9/YZJIkaSmtGx1AWi2S7JrkxCRnTz9enmTX0bkkSZIkzYwLgd1Gh5DWoG8DO84b3xL41qAskiRpmbgCTFokSd7D5IL2jdOp32byhNmjxqWSJEmSNCuSHAa8n8l1w/x9gx8xLJS0BiR5H3A48BEm7UePAc4CvgnQ9rhx6SRJ0lKxACYtkiTntT1kS3OSJEmS1qYkXwJeA1wA3Dg33/YTw0JJa0CSJ27ufNs3bu68JElambYfHUBaRX6U5Ki2nwJIciTwo8GZJEmSJM2Oa9r+zegQ0lqSZDvgl9oeOzqLJElaXhbApMXzdOCN032/AlwOPGloIkmSJEmz5IwkLwZO5aYtEM8ZF0la3dquT7JXkh3aXj86jyRJWj62QJQWWZJdANpeOTqLJEmSpNmR5OMbmW7bo5c9jLSGJHkT8LNMis9Xz823PXFYKEmStORcASZtoySPb/uWJM/aYB7wD2pJkiRJ/+kpbS+dP5HkrqPCSGvIV6cf64DbDM4iSZKWiQUwadvdavp5Y39Eu8RSkiRJ0px3A4duMHcKcO8BWaQ1o+2fASTZue01o/NIkqTlYQFM2kZtXzM9/GjbM+efS3LkgEiSJEmSZkiS/YF7ALsmedS8U7sAO45JJa0dSY4AXg/cGviZJAcDT2v7+2OTSZKkpWQBTFo8r+Snn+bc2JwkSZKktWU/4GHAbsDD581fBTx1SCJpbflr4JeZ7AFG2y8m+fmxkSRJ0lKzACZto+mTZPcD9thgH7BdgO3GpJIkSZI0K9q+H3h/kiPafmZ0HmktavuNub26p9aPyiJJkpbHutEBpFVgByZtFLZnsg/Y3MeVwKMH5pIkSZI0Wx6ZZJckt0jysST/keTxo0NJa8A3ktwP6PTn79nARaNDSZKkpZW2ozNIq0KSvdpeluTWAG1/ODqTJEmSpNmR5Ly2hyR5JJOWiM8CPtn24MHRpFUtye7AK4BfBAJ8GDiu7eVDg0mSpCVlC0Rp8dwmybnA7QCSfA94YtsLx8aSJEmSNCNuMf38UOCUtlds0JJN0tLYr+2x8yeSHAmcOSiPJElaBrZAlBbPa4Fntd2r7V7AH03nJEmSJAngA0kuBu4NfCzJHsC1gzNJa8ErFzgnSZJWEVsgSoskyRc3bF2ysTlJkiRJa1eS2wFXtF2f5FbAbdp+d3QuaTVKcgRwP+B44H/PO7UL8Eiv1yVJWt1cASYtnkuT/GmSvacfzwcuHR1KkiRJ0mxIsjPw+8Crp1N3AQ4bl0ha9XYAbs1kC5DbzPu4Enj0wFySJGkZuAJMWiRJbgv8GXDUdOoM4IS2PxiXSpIkSdKsSPJO4AvAE9rec1oQ+3TbQwZHk1a1JHu1vWx0DkmStLwsgEmSJEmStAySnN32sCTntr3XdM626dISS3J34NnA3kxWgwHQ9uhRmSRJ0tLbfssvkbQ5SU7d3Pm2j1iuLJIkSZJm2vVJdgIKkGRf4LqxkaQ14RTg74CTgPWDs0iSpGViAUzadkcA3wDeDnwOyNg4kiRJkmbUCcDpwJ5J3gocCTxpZCBpjbih7au3/DJJkrSa2AJR2kZJtgOOAR4HHAR8CHh72y8NDSZJkiRp5iS5PXBfJg/Ofbbt9wZHkla9JCcA/w68l3mrLttePiqTJElaehbApEWU5JZMCmEvA/6s7asGR5IkSZI0I5J8AHgbcGrbq0fnkdaKJF/byHTb3nXZw0iSpGVjAUxaBNPC10OZFL/2Bk4F3tD2WyNzSZIkSZodSR4APJbJtcPngXcAH2x77dBgkiRJ0ipkAUzaRkneBNwT+AfgHW0vHBxJkiRJ0gybtlE/Gngq8OC2uwyOJK1KSf647Uunx49pe8q8c3/R9rnj0kmSpKVmAUzaRkluBObal8z/gQqTlgpezEqSJEkCIMlOwMOZrAQ7lMkKsD8Ym0panZKc0/bQDY83NpYkSavP9qMDSCtd23WjM0iSJEmafUneBfwccDrwKuATbW8cm0pa1bKJ442NJUnSKmMBTJIkSZKk5fF64HFt148OIq0R3cTxxsaSJGmVsQWiJEmSJEnLJMn9gL2Z90Bq2zcNCyStYknWM9myIMBOwDVzp4Ad295iVDZJkrT0XAEmSZIkSdIySPJmYF/gPGBuFVgBC2DSEmi73egMkiRpHFeASZIkSZK0DJJcBBxQL8QlSZKkJbdudABJkiRJktaIC4E7jQ4hSZIkrQW2QJQkSZIkaXnsDvxzkrOA6+Ym2z5iXCRJkiRpdbIAJkmSJEnS8jhhdABJkiRprXAPMEmSJEmSJEmSJK0qrgCTJEmSJGkJJbkK2NjTpwHadpdljiRJkiSteq4AkyRJkiRJkiRJ0qqybnQASZIkSZIkSZIkaTFZAJMkSZIkSZIkSdKqYgFMkiRJkiRJkiRJq4oFMEmSJElaRkl+uJWv/4UkH7yZ/9bxSXa+Oe+VJEmSpJXMApgkSZIkrV7HAxstgCXZbpmzSJIkSdKysQAmSZIkSQNMV3b9U5J3J7k4yVuTZHruwdO5c4BHzXvPCUmePW98YZK9k9wqyYeSfHE699gkxwF3AT6e5OPT1/8wycuTfBF4XpL3zftaxyR573L9/yVJkiRpKW0/OoAkSZIkrWH3Au4BfBs4EzgyydnA64Cjga8A71zA13kw8O22DwVIsmvbK5I8C3hg2+9NX3cr4HNt/2habLsoyR5t/wP4HeANi/mfkyRJkqRRXAEmSZIkSeOc1fabbW8EzgP2BvYHvtb2X9oWeMsCvs4FwDFJ/jLJ/dtesYnXrQfeAzD92m8GHp9kN+AI4LRt++9IkiRJ0mywACZJkiRJ41w373g9W+7ScQM3vY7bEaDtl4FDmRTCXpTkBZt4/7Vt188bnww8HngccErbG7YiuyRJkiTNLAtgkiRJkjRbLgb2TrLvdPy4eee+zqTQRZJDgX2mx3cBrmn7FuBlc68BrgJus6l/qO23mbRffD6TYpgkSZIkrQruASZJkiRJM6TttUl+D/hQkmuAM/hJEes9wBOSfAn4HPDl6fyBwMuS3Aj8GHj6dP61wOlJvt32gZv4J98K7NH2oiX470iSJEnSEJm0fZckSZIkrUVJXgWc2/b1o7NIkiRJ0mKxACZJkiRJa1SSLwBXA8e0vW5Lr5ckSZKklcICmCRJkiRJkiRJklaVdaMDSJIkSZIkSZIkSYvJApgkSZIkSZIkSZJWFQtgkiRJkiRJkiRJWlUsgEmSJEmSJEmSJGlVsQAmSZIkSZIkSZKkVcUCmCRJkiRJkiRJklaV/w8ih7buYCoIcgAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##K-MEANS METHOD"
+      ],
+      "metadata": {
+        "id": "ApA9Z4pPKu2h"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "kmeans = KMeans(n_clusters=4)\n",
+        "k_fit = kmeans.fit(df[num_deg])\n",
+        "kumeler = kmeans.labels_\n",
+        "df[\"Class\"]=kumeler\n",
+        "df = pd.DataFrame(df)\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 597
+        },
+        "id": "sZoR-27nKrWw",
+        "outputId": "77ec6624-22fd-4bf6-ebc9-063b87d83d37"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                       Job Title              Salary Estimate                                    Job Description  Rating                                Company Name         Location    Headquarters          Size  Founded   Type of ownership                          Industry                        Sector                           Revenue                                        Competitors  Hourly  Employer provided  Lower Salary  Upper Salary  Avg Salary(K)  \\\n",
+              "index                                                                                                                                                                                                                                                                                                                                                                                                                                                                         \n",
+              "0                 Data Scientist   $53K-$91K (Glassdoor est.)  Data Scientist\\nLocation: Albuquerque, NM\\nEdu...   3.800                      Tecolote Research\\n3.8  Albuquerque, NM      Goleta, CA   501 - 1000      1973   Company - Private               Aerospace & Defense           Aerospace & Defense         $50 to $100 million (USD)                                                 -1       0                  0            53            91         72.000   \n",
+              "1      Healthcare Data Scientist  $63K-$112K (Glassdoor est.)  What You Will Do:\\n\\nI. General Summary\\n\\nThe...   3.400  University of Maryland Medical System\\n3.4    Linthicum, MD   Baltimore, MD       10000+      1984  Other Organization  Health Care Services & Hospitals                   Health Care            $2 to $5 billion (USD)                                                 -1       0                  0            63           112         87.500   \n",
+              "2                 Data Scientist   $80K-$90K (Glassdoor est.)  KnowBe4, Inc. is a high growth information sec...   4.800                                KnowBe4\\n4.8   Clearwater, FL  Clearwater, FL   501 - 1000      2010   Company - Private                 Security Services             Business Services        $100 to $500 million (USD)                                                 -1       0                  0            80            90         85.000   \n",
+              "3                 Data Scientist   $56K-$97K (Glassdoor est.)  *Organization and Job ID**\\nJob ID: 310709\\n\\n...   3.800                                   PNNL\\n3.8     Richland, WA    Richland, WA  1001 - 5000      1965          Government                            Energy  Oil, Gas, Energy & Utilities  $500 million to $1 billion (USD)  Oak Ridge National Laboratory, National Renewa...       0                  0            56            97         76.500   \n",
+              "4                 Data Scientist  $86K-$143K (Glassdoor est.)  Data Scientist\\nAffinity Solutions / Marketing...   2.900                     Affinity Solutions\\n2.9     New York, NY    New York, NY     51 - 200      1998   Company - Private           Advertising & Marketing             Business Services          Unknown / Non-Applicable               Commerce Signals, Cardlytics, Yodlee       0                  0            86           143        114.500   \n",
+              "\n",
+              "                                 company_txt Job Location  Age  Python  spark  aws  excel  sql  sas  keras  pytorch  scikit  tensor  hadoop  tableau  bi  flink  mongo  google_an   job_title_sim seniority_by_title Degree  Class  \n",
+              "index                                                                                                                                                                                                                               \n",
+              "0                          Tecolote Research           NM   48       1      0    0      1    0    1      0        0       0       0       0        1   1      0      0          0  data scientist                 na      M      3  \n",
+              "1      University of Maryland Medical System           MD   37       1      0    0      0    0    0      0        0       0       0       0        0   0      0      0          0  data scientist                 na      M      3  \n",
+              "2                                    KnowBe4           FL   11       1      1    0      1    1    1      0        0       0       0       0        0   0      0      0          0  data scientist                 na      M      3  \n",
+              "3                                       PNNL           WA   56       1      0    0      0    0    0      0        0       0       0       0        0   0      0      0          0  data scientist                 na     na      3  \n",
+              "4                         Affinity Solutions           NY   23       1      0    0      1    1    1      0        0       0       0       0        0   0      0      0          0  data scientist                 na     na      0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cb528353-e1c2-4f86-85d5-7e2c53512d06\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>Job Title</th>\n",
+              "      <th>Salary Estimate</th>\n",
+              "      <th>Job Description</th>\n",
+              "      <th>Rating</th>\n",
+              "      <th>Company Name</th>\n",
+              "      <th>Location</th>\n",
+              "      <th>Headquarters</th>\n",
+              "      <th>Size</th>\n",
+              "      <th>Founded</th>\n",
+              "      <th>Type of ownership</th>\n",
+              "      <th>Industry</th>\n",
+              "      <th>Sector</th>\n",
+              "      <th>Revenue</th>\n",
+              "      <th>Competitors</th>\n",
+              "      <th>Hourly</th>\n",
+              "      <th>Employer provided</th>\n",
+              "      <th>Lower Salary</th>\n",
+              "      <th>Upper Salary</th>\n",
+              "      <th>Avg Salary(K)</th>\n",
+              "      <th>company_txt</th>\n",
+              "      <th>Job Location</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Python</th>\n",
+              "      <th>spark</th>\n",
+              "      <th>aws</th>\n",
+              "      <th>excel</th>\n",
+              "      <th>sql</th>\n",
+              "      <th>sas</th>\n",
+              "      <th>keras</th>\n",
+              "      <th>pytorch</th>\n",
+              "      <th>scikit</th>\n",
+              "      <th>tensor</th>\n",
+              "      <th>hadoop</th>\n",
+              "      <th>tableau</th>\n",
+              "      <th>bi</th>\n",
+              "      <th>flink</th>\n",
+              "      <th>mongo</th>\n",
+              "      <th>google_an</th>\n",
+              "      <th>job_title_sim</th>\n",
+              "      <th>seniority_by_title</th>\n",
+              "      <th>Degree</th>\n",
+              "      <th>Class</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>index</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></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>0</th>\n",
+              "      <td>Data Scientist</td>\n",
+              "      <td>$53K-$91K (Glassdoor est.)</td>\n",
+              "      <td>Data Scientist\\nLocation: Albuquerque, NM\\nEdu...</td>\n",
+              "      <td>3.800</td>\n",
+              "      <td>Tecolote Research\\n3.8</td>\n",
+              "      <td>Albuquerque, NM</td>\n",
+              "      <td>Goleta, CA</td>\n",
+              "      <td>501 - 1000</td>\n",
+              "      <td>1973</td>\n",
+              "      <td>Company - Private</td>\n",
+              "      <td>Aerospace &amp; Defense</td>\n",
+              "      <td>Aerospace &amp; Defense</td>\n",
+              "      <td>$50 to $100 million (USD)</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>53</td>\n",
+              "      <td>91</td>\n",
+              "      <td>72.000</td>\n",
+              "      <td>Tecolote Research</td>\n",
+              "      <td>NM</td>\n",
+              "      <td>48</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>data scientist</td>\n",
+              "      <td>na</td>\n",
+              "      <td>M</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Healthcare Data Scientist</td>\n",
+              "      <td>$63K-$112K (Glassdoor est.)</td>\n",
+              "      <td>What You Will Do:\\n\\nI. General Summary\\n\\nThe...</td>\n",
+              "      <td>3.400</td>\n",
+              "      <td>University of Maryland Medical System\\n3.4</td>\n",
+              "      <td>Linthicum, MD</td>\n",
+              "      <td>Baltimore, MD</td>\n",
+              "      <td>10000+</td>\n",
+              "      <td>1984</td>\n",
+              "      <td>Other Organization</td>\n",
+              "      <td>Health Care Services &amp; Hospitals</td>\n",
+              "      <td>Health Care</td>\n",
+              "      <td>$2 to $5 billion (USD)</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>63</td>\n",
+              "      <td>112</td>\n",
+              "      <td>87.500</td>\n",
+              "      <td>University of Maryland Medical System</td>\n",
+              "      <td>MD</td>\n",
+              "      <td>37</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>data scientist</td>\n",
+              "      <td>na</td>\n",
+              "      <td>M</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Data Scientist</td>\n",
+              "      <td>$80K-$90K (Glassdoor est.)</td>\n",
+              "      <td>KnowBe4, Inc. is a high growth information sec...</td>\n",
+              "      <td>4.800</td>\n",
+              "      <td>KnowBe4\\n4.8</td>\n",
+              "      <td>Clearwater, FL</td>\n",
+              "      <td>Clearwater, FL</td>\n",
+              "      <td>501 - 1000</td>\n",
+              "      <td>2010</td>\n",
+              "      <td>Company - Private</td>\n",
+              "      <td>Security Services</td>\n",
+              "      <td>Business Services</td>\n",
+              "      <td>$100 to $500 million (USD)</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>80</td>\n",
+              "      <td>90</td>\n",
+              "      <td>85.000</td>\n",
+              "      <td>KnowBe4</td>\n",
+              "      <td>FL</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>data scientist</td>\n",
+              "      <td>na</td>\n",
+              "      <td>M</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Data Scientist</td>\n",
+              "      <td>$56K-$97K (Glassdoor est.)</td>\n",
+              "      <td>*Organization and Job ID**\\nJob ID: 310709\\n\\n...</td>\n",
+              "      <td>3.800</td>\n",
+              "      <td>PNNL\\n3.8</td>\n",
+              "      <td>Richland, WA</td>\n",
+              "      <td>Richland, WA</td>\n",
+              "      <td>1001 - 5000</td>\n",
+              "      <td>1965</td>\n",
+              "      <td>Government</td>\n",
+              "      <td>Energy</td>\n",
+              "      <td>Oil, Gas, Energy &amp; Utilities</td>\n",
+              "      <td>$500 million to $1 billion (USD)</td>\n",
+              "      <td>Oak Ridge National Laboratory, National Renewa...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>56</td>\n",
+              "      <td>97</td>\n",
+              "      <td>76.500</td>\n",
+              "      <td>PNNL</td>\n",
+              "      <td>WA</td>\n",
+              "      <td>56</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>data scientist</td>\n",
+              "      <td>na</td>\n",
+              "      <td>na</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Data Scientist</td>\n",
+              "      <td>$86K-$143K (Glassdoor est.)</td>\n",
+              "      <td>Data Scientist\\nAffinity Solutions / Marketing...</td>\n",
+              "      <td>2.900</td>\n",
+              "      <td>Affinity Solutions\\n2.9</td>\n",
+              "      <td>New York, NY</td>\n",
+              "      <td>New York, NY</td>\n",
+              "      <td>51 - 200</td>\n",
+              "      <td>1998</td>\n",
+              "      <td>Company - Private</td>\n",
+              "      <td>Advertising &amp; Marketing</td>\n",
+              "      <td>Business Services</td>\n",
+              "      <td>Unknown / Non-Applicable</td>\n",
+              "      <td>Commerce Signals, Cardlytics, Yodlee</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>86</td>\n",
+              "      <td>143</td>\n",
+              "      <td>114.500</td>\n",
+              "      <td>Affinity Solutions</td>\n",
+              "      <td>NY</td>\n",
+              "      <td>23</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>data scientist</td>\n",
+              "      <td>na</td>\n",
+              "      <td>na</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb528353-e1c2-4f86-85d5-7e2c53512d06')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-cb528353-e1c2-4f86-85d5-7e2c53512d06 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-cb528353-e1c2-4f86-85d5-7e2c53512d06');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(4)\n",
+        "plt.figure(figsize=(12,9))\n",
+        "sns.barplot(x=df.groupby(\"Class\").mean()[\"Avg Salary(K)\"].sort_values(ascending=False).index,y=df.groupby(\"Class\").mean()[\"Avg Salary(K)\"].sort_values(ascending=False))\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 568
+        },
+        "id": "iCeIvrlkK1l_",
+        "outputId": "e2e092e5-75c9-44fc-8620-27a05f4775a4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x648 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##FORECASTING AVG SALARY BASED ON PROGRAMMING KNOWLEDGE"
+      ],
+      "metadata": {
+        "id": "OjmaJH6mLD7b"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "new_user_3_knowledge=[\"Python\",\"bi\",\"tableau\"] #I chosen 3 programming knowledge for user.\n",
+        "print(r\"Users Estimated Salary is :\",calculate_estimated_salary(df, new_user_3_knowledge))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EczZ54XQLFbP",
+        "outputId": "7685d563-27b5-40a8-87bd-cfebef820163"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Users Estimated Salary is : 105.72368421052632\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file