diff --git a/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb new file mode 100644 index 000000000..11960cd8b --- /dev/null +++ b/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "b1e38707", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XcxYBAADgFMEiAAAAnCJYBAAAgFMEiwAAAPfh119/NdUMChQoYEpmOSpflpDWX61evbqp5KCVDfTymwnp1ZW0ekNAQIDUqVPHVNJIWNmgf//+plqElgHTKhxaw9eWXqlMS7RpwX2tnqDVKu5WvswRgkUAAID7EB4ebsqkaXCXFFrKTQM4LZ22fft2U++0V69edrVptS6sXk5W67Ru3brVnF8vy2l7udSBAweaklfffPONqZF6+vRpUzrNQuvT6uNonV29YISWbNOgVC9zmxyshgYAAEglXl5epqav1tF1Rq+Y9OOPP5oLIVjoJU21Fu+yZcvMvmYSa9WqZYrlK70ClV4KVC+Jqldb0vqywcHB5vrzTzzxhPWqVHqlMK1P+9BDD5nL3mrtWg0i8+XLZ9ro1cX08S9cuGDquyYFmUUAAIAE9CpcepWu6zab5cpc90uDuYSXQ9WsoR5XmgnUCwjYttErg+m+pY3eHh0dbddGLzahlwy1tNF/9apilkDR8jj6XHbv3u15V3DJXKRzWncBbtR+bt+07gLc6MT1O5ezQ8Zwdnria47jwXVwRc8HMnZ4tUcZc2UjWzokfL9XuFJ6+VHbAE7pvgZxejUtvZKYDiE7amO5pr2eQzODOXLkSNRGb7vb41hu87hgEQAAIL3QS8bqnEFbuhglIyJYBAAAHsnLy3Wz6TQwdFVwGBISkmjVsu4HBQVJ5syZzXXvdXPURu9rOYcOV+s8R9vsYsI2CVdQW85paZMUzFkEAABwo7p168qqVavsjq1cudIcVzq8XKNGDbs2usBF9y1t9HZfX1+7Nvv37zelcixt9N9du3bZraDWx9GgtHz58knuL5lFAADgkbzSSc7r5s2bcujQIbvSOFoSJ1euXGbBiQ5pnzp1SubOnWtu79u3r1nlPHToUOnRo4esXr1avv76a7NC2kKHwLt16yY1a9aU2rVry5QpU0yJnu7du5vbs2fPLj179jTt9HE0ANSV0hog6kpo1bx5cxMUPvvsszJhwgQzT3HkyJGmNmNysqYEiwAAAPfhr7/+MjUTLSxzHTXY07qGZ86cMRk/i2LFipnAUOskTp06VQoVKiSffvqpWals0bFjR1PeRmsiapBXtWpVU1bHdsHK+++/b1ZJazFuXamt9//www+tt+tQ9g8//CD9+vUzQWRgYKDp07hx4zyzziKroTMWVkNnLKyGzlhYDZ2xpOVq6Kyh3Vx27pvH5rjs3J6GzCIAAPBIrlzggn/wKgMAAMApMosAAMBjL60H1yOzCAAAAKfILAIAAA9FzssdeJUBAADgFJlFAADgkVgN7R68ygAAAHCKzCIAAPBIZBbdg1cZAAAATpFZBAAAHsmLnJdbECwCAACPxDC0e/AqAwAAwCkyiwAAwCORWXQPXmUAAAA4RWYRAAB4JDKL7sGrDAAAAKfILAIAAI/kJV5p3YUMgcwiAAAAnCKzCAAAPBJzFt2DYBEAAHgkgkX34FUGAACAU2QWAQCARyKz6B68ygAAAHCKzCIAAPBQ5LzcgVcZAAAATpFZBAAAHok5i+7BqwwAAACnyCwCAACPRGbRPQgWAQCAR/JigNQteJUBAADgFJlFAADgkRiGdg9eZQAAADhFZhEAAHgkLy+vtO5ChkBmEQAAAE6RWQQAAB6JOYvuwasMAAAAp8gsAgAAj0SdxXQcLMbGxsrnn38uq1atkvPnz0tcXJzd7atXr06t/gEAADjEMHQ6Dhb/85//mGCxdevWUrFiRVYjAQAAPKBSFCx+9dVX8vXXX0urVq1Sv0cAAABJQGbRPVL0Kvv5+UnJkiVTvzcAAADw/GBx8ODBMnXqVImPj0/9HgEAACRxgYurNqRgGPrxxx9PtIjl559/lgoVKoivr6/dbYsXL07qaQEAAJCOJTl0zp49u93Wvn17adSokeTJkyfRbQAAAC6ncxZdtSXTjBkzJDQ0VAICAqROnTqyadMmp22jo6Nl3LhxUqJECdO+SpUqsmzZMrs2N27ckJdfflmKFi0qmTNnlnr16snmzZvtn76Xl8Ptvffes7bRPiW8/Z133nFNZnH27NnJOjGcq1+7rAzs+6hUr1Rc8ufLKU/1miTfr/grrbuFZDq/Zo2cW7lCoq9dk8yFCkmRTp0lsFgxh23jY2PkzM/L5NKGPyT66lUJCAmRgu0fl+wVK1rbxEZEyOmlS+Xq9m0SfeOGZClcWAp37CSBoaFufFZwpn1ofulcsqDk8veTw9fDZcquw7L36k2HbX28vOTZUoWkZeG8kifAX07cvC0f7Tkqmy5ctbbJ7OMjvcoWkYb5c0tOf185cC1cPvj7iOxzck6kT7UqhUivJytJhVK5JV/uQOk35hf55Y/jad0tuNnChQtl0KBBMnPmTBMoTpkyRVq0aCH79++XvHnzJmo/cuRImT9/vnzyySdStmxZWb58uUnC/fHHH1KtWjXTplevXvL333/LvHnzpECBAqZ9s2bNZM+ePVKwYEHT5syZM3bn1RHfnj17SocOHeyOa2Dau3dv6362bNmS9fxSNCj/r3/9S65e/eeXnsX169fNbbi7wCz+smtPmLw8clZadwUpdHnzZjn57TeSv/WjUm7ESMlSqLAc/GCqRF+/7rD9qSVL5eJvv5qAssKYsRLcsKEcnvmR3AoLs7Y5PneuXN+7R0K795Dyo0ZLUPnycuD9yRJ15Yobnxkc+VeBPPJihWLy+f4w6bVumxy6Fi6THqooOfzsp+BY9C5bVNoWDZEpu47Is2u2yNLjZ+St2uWkVFCgtc2rVUtKreAc8sbWA9Jt7TbZfOGqvF+3ouQJ8HPjM8P9yhyQSfYduSxjp29I665k2NXQrtqSY/LkySYY6969u5QvX94EjVmyZJFZsxx/zmsA+Nprr5mqMsWLF5d+/fqZrydNmmRuv337tixatEgmTJggDRs2NIuKx4wZY/796KOPrOcJCQmx25YuXSpNmjQx57SlwaFtu8DAf34XuSxYXLt2rURFRSU6HhERIb/99ltKTpmhrFi7Q8ZO/Fq+W0420VOd+2Wl5GnQQPLUry+ZCxSQIl26iLefn1z643eH7S9v/FNCWj4i2StVEv/gYAlu1NhkFc+tXGluj4uKkivbtkqhDh0kW+nSEpA3rxRo09b8e2HdOjc/OyTUsURB+T7srPx04rwcu3lbJu48JBGxsdK6SD6H7VsUDpZ5B0/Kn+evyJlbkbLk2FnZcO6KdCp5Jxvg5+0tjfLnkY/2HJMdl6/LqfAImb0/zPz7WGiIm58d7sevm0/K+59vkZW/k01MC86GYVNjS6qoqCjZsmWLyfpZeHt7m/0NGxz/EREZGWmGn23pUPP69evN1zExMeYCKHdrk9C5c+fkxx9/NJnFhHTYOXfu3CZrqUPUen6X1VncuXOn9WtNg549e9a6r09Kx9stqVHgQRUXE2MygvkfecR6zMvbW7KVLSc3jxxxeh/vBAvBvH395ObhQ+breL0KUlyceGWyb+Pl62ttg7SRyctLSmfPKvMPnrAe0zoQf128KhVyOh7K8fX2lqgEV7bS/Uq5gqzD1Jm8vRK1iYyNlcq5mPcNpAca0Olmy9/f32y2Ll68aGKgfPns/3jU/X379okjOkSt2UjNGuq8Rb0ini4O1vNYMoF169aV8ePHS7ly5cy5vvzySxN8OitdOGfOHHO/hAuSX3rpJalevbrkypXLDHMPHz7cDF/r47skWKxatao14nY03KwR77Rp05JzSsDjxNy8aQK7TNnufPBb+AZlk4iz9vNHLILKVzDZyKylSpnM4o19+0wmUf6//JRPQIAEFi8uZ376UQLy5xffoCC5vGmThB85Iv4O5rvAfbL7+ZrA7nJktN3xK5HRUjRrFof32XT+inQsXkB2XLpmsoU1gnNIw5Dc4v3/2YrbsbGy6/J16Va6iBy7sV+uREZJs0LBUiFXkJwKv+2W5wU8CFxZ4ubtt9+WsWPH2h0bPXq0GQ6+X1p+UIetdb6ixlQaMOoQtu2wtQ5V9+jRwyThfHx8TMDXuXNnk8V0RO/bpUuXRNlInUtpUblyZVMru0+fPub5JQx8UyVYPHr0qKmtqGPhusonODjYeps+uE7i1CeUkmg9Pj5WvLzufV/AExXu2FGOz5sru0eP0nETEzDmqVdfLtoMWxfr0UOOzZkju14dqmMYkqVIEclVq7bcCmN4y9PoQpWhVUrJ/H/VMH8PnL51W346cc5u2FrnKg6vWkqWtKgtMXHxcuDaTVl16oLJYgJIe5qBsw20lKPgSqvCaOyjw8C2dF/nBzqi8dOSJUvM9L1Lly6ZBSzDhg2zm2uoAeS6deskPDzcrAnJnz+/dOzYMdF8RKVTAHUxjS60uRddgKPD0MeOHZMyZcpIqgeLunxbxSUYOkmNaN0nqIL4Zq90X+cF3CFT1qwmmIu5Yb+YJfr6DfF1UjrKN1s2KflCf4mLjjaZSd8cOeTU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDdzvWlS0CeZy+dtPEdAVzJciEs/dVlejYuS1zXvFz9tLgvx85WJElPQtFyqnwyOsbU7fipABf+ySAB9vCczkI5cio2VMjTJy5tY/bQCk3eX+HA05O6LJsho1apih5Mcee8waJ+n+iy++KHejWUDNHGopHV3Q8tRTTyVqo4tRdLty5YpZNa2LXhL67LPPTB+0BM+9bN++3cypdLRKO1WvDW07bzEsLCzRYpe2bdsmO1rPW6HX/XQFcBvvTJlM1u/63n2So2o165zDG/v2St4mTe5+X19f8cuZ05TSubptq+SsUTNRGx9/f7PF6F+Te3ZLwcftSyDAvWLi72T9auTJIb+dvWyO6WCy7i8+6njagUVUXLwJFHWOYqMCuWXNqYuJ2kTExpktq6+P1M6b05TYAeBZBg0aJN26dZOaNWtK7dq1TekczQjq0LLq2rWrCQo1WaY2btwop06dMtP79F8d2tYAc+jQodZzamCoo7ma/Tt06JAMGTLEDFtbzmmhWcdvvvnGupLals5x1MfSFdI6n1H3Bw4cKM8884zkzJnTtcHikSNHTD2gXbt2mbF2y2X/LKuHLBM0kxOtZ6QhaC2dU8JmxWNo4WCpXL6oXLl6U06cvpSmfUPS5Gv2bzn2+WwJDC0qWUKLyflVv5gVzbnr1Te3H509S/xy5DC1FFX40SMSdeWqqZ0YdfWqnPn+e/Nzk69FC+s5r+3ebeYwag3GyPPn5eSib83XeerXS7PniTsWHj4lr1UrLfuu3ZS9V27Ik8ULmDqJOrSsRlQrLRcjIuXjvXemDJTPkVXyZPaXg9duSnCAv/QoU8RcQOyLQyet56wdnMP8eyL8thQMzCwvlA+VsBu35Kew82n0LJESWQIySdEC/8xfLhSSVcoVzyVXb0TKmQvhadq3DCEZq5ZdqWPHjnLhwgUZNWqUWfyrQaAu+rUsetHEmmbzLHT4WWstajyVNWtWUzZH5yjmyHHn94K6du2aSa6dPHnSLE7R2olvvvlmoqvmffXVV+bzROczJqSxlt6uwahO/ytWrJgJFhMm7O7FKz4FF3hu06aNGZ//9NNPzQPr/EUdc9drRk+cOFEefvjh5J5SMhdJ/CQfVA8/VE5WfD0q0fF536yT5wfPlIyg/dy+4unOr1kt51asMLUV7xTl7iSBxe7MJdk/aaL4584toc/d+QvwxoH9EvbFFxJ54YJ4+/ubEjoaSGpAaXH5r7/k1P8Wm6LdPlmySM7q1aXgY4+JT2bHiyg8yYnrnv/H4OM2RbkPXQ+XqbsOy57/L6D9Qb1KcvZWhLy1/aDZr5o7SAZXLin5swTI7ZhYU0Jn5p5jcinyn1GYJgXySJ9yRU0weSM6RtaeuSif7D0u4TF3/2PbE5ydvl8yitqVQ2TBxNaJji9ecUBenZgxSskdXJG4VIu7lK79ocvOfWDTCy47t6dJUbCokzn12tC6qkYv76fBoqZJ9ZgGjNu2bUt2RzJSsIgHI1hExgoWkXQZKVhEGgeLD7kwWPyTYNEiRTNDdZjZcqkYDRxPnz5tXQCjq3EAAADcMgztqg33N2exYsWKsmPHDjMErUuwdWWOrgb673//63BJNwAAADJQsKiTMnWVj9ISODqHUecp6qVkdCIlAACAy5EBTL/Bol6mxqJUqVLmcjaXL182y7CTcz1FAAAAPEDBol52JilsL1cDAADgEq6ryY2UBouff/65WcRSrVo1a21FAAAAPLiSFSz269dPvvzyS3ONaK0grhXAtVAkAACAu8Uz9S39JXBnzJghZ86cMZej+f7776Vw4cLmOoaWS9IAAAAgg4/266Vj9JIyK1euNNeGrlChgrzwwgsSGhoqN2/euZoBAACAy3m5cMP9rYa20OscWq4Nfa/rQQMAAKQqb6K6dJlZ1AtR67zFf//731K6dGnZtWuXTJ8+3VwkWy+GDQAAgAyaWdThZi26rXMVtYyOBo16uT8AAAC3Y4FL+gsWZ86cKUWKFDGX9Fu3bp3ZHFm8eHFq9Q8AAACeEix27dqVK7QAAID0gZAkfRblBgAAQMZxX6uhAQAA0gyrod2CqyoCAADAKTKLAADAM7GOwi0IFgEAgGciVnQLhqEBAADgFJlFAADgmVjg4hZkFgEAAOAUmUUAAOCZSCy6BZlFAAAAOEVmEQAAeKR4Sue4BZlFAAAAOEVmEQAAeCZWQ7sFmUUAAAA4RWYRAAB4JhKLbkGwCAAAPBMLXNyCYWgAAAA4RWYRAAB4Jha4uAWZRQAAADhFZhEAAHgmEotuQWYRAAAATpFZBAAAnonV0G5BZhEAAABOkVkEAACeicyiWxAsAgAAz8T4qFvwMgMAAMApMosAAMAzMQztFmQWAQAA4BSZRQAA4JlILLoFmUUAAAA4RbAIAAA8Ury3l8u25JoxY4aEhoZKQECA1KlTRzZt2uS0bXR0tIwbN05KlChh2lepUkWWLVtm1+bGjRvy8ssvS9GiRSVz5sxSr1492bx5s12b5557Try8vOy2li1b2rW5fPmydOnSRYKCgiRHjhzSs2dPuXnzZrKeG8EiAADAfVi4cKEMGjRIRo8eLVu3bjXBX4sWLeT8+fMO248cOVI+/vhjmTZtmuzZs0f69u0r7du3l23btlnb9OrVS1auXCnz5s2TXbt2SfPmzaVZs2Zy6tQpu3NpcHjmzBnr9uWXX9rdroHi7t27zbl++OEH+fXXX+X5559P1vPzio+Pj5d0IHORzmndBbhR+7l907oLcKMT133Sugtwo7PT96d1F+BGB1f0TLPHLvG0fWCUmg5/kfS4pE6dOlKrVi2ZPn262Y+Li5PChQvLgAEDZNiwYYnaFyhQQEaMGCH9+/e3HuvQoYPJIM6fP19u374t2bJlk6VLl0rr1q2tbWrUqCGPPPKIvPHGG9bM4tWrV2XJkiUO+7V3714pX768yUjWrFnTHNMMZqtWreTkyZOmH0lBZhEAAHgmLxduSRQVFSVbtmwxWT8Lb29vs79hwwaH94mMjDTDz7Y0UFy/fr35OiYmRmJjY+/axmLt2rWSN29eKVOmjPTr108uXbpkvU0fX4eeLYGi0n5p/zZu3Jjk50iwCAAA4CCgu379ut2mxxK6ePGiCezy5ctnd1z3z5496/DcOkQ9efJkOXjwoMlC6hDx4sWLzTCy0qxi3bp1Zfz48XL69Glzfs04avBnaWMZgp47d66sWrVK3n33XVm3bp3JPGp7pY+vgaStTJkySa5cuZz2zRGCRQAA4Jl0IYqLtrfffluyZ89ut+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1JmCBQsWFH9/f/nggw+kc+fOdm06deokbdu2lUqVKsljjz1m5iTqkLNmG1MTwSIAAEACw4cPl2vXrtlteiyhPHnyiI+Pj5w7d87uuO6HhIQ4PHdwcLCZZxgeHi7Hjx+Xffv2SdasWaV48eLWNrpSWjOFunL5xIkTZnW1rqK2bZOQ3qb9OXTokNnXx0+4yEaHuHWFtLO+OUKwCAAAPPdyfy7aNJun5WaCbDY9lpBmBnXhiQ4FW+jQsu7rUPLd6JxEzRxqALdo0SJp165dojaBgYGSP39+uXLliixfvtxhGwtdtKJzFrW90sfXBTA6p9Ji9erVpn+6KCepuIILAADAfRg0aJB069bNLCSpXbu2TJkyxWQNdWhZde3a1QSFlmFsXVyiJXCqVq1q/h0zZowJ4IYOHWo9pwaGOgytC1c0UzhkyBAzbG05p2Ycx44da1ZRa5bw8OHD5v4lS5Y0cyJVuXLlzLzG3r17y8yZM01mUoe8dfg6qSuh01WwSCmVjOV/XWemdRfgRrfDxqZ1F+BGJb4MS+suIKNIJ5f769ixo1y4cEFGjRplFo5oEKglaiyLXsLCwuzmGkZERJhai0eOHDHDz1rKRuco6splC8uwt2YLdUGKBoVvvvmm+Pr6mtt16Hvnzp0yZ84ckz3U4E9rMeqiGNsM6IIFC0yA2LRpU9MHPY/Of/TIOotPr12X1l2AGxEsZiwEixlLic5/pXUX4EaHv3w6zR67RLeFLjv34TkdXXZuT5NuMosAAADJkoLL8iH5CBYBAIBnIlh0C1ZDAwAAwCkyiwAAwCPFk1h0CzKLAAAAcIrMIgAA8EzMWXQLMosAAABwiswiAADwTHppPrgcmUUAAAA4RWYRAAB4JuYsugXBIgAA8EyMj7oFLzMAAACcIrMIAAA8Ewtc3ILMIgAAAJwiswgAADwTC1zcgswiAAAAnCKzCAAAPFI8cxbdgswiAAAAnCKzCAAAPBMpL7cgWAQAAJ6JBS5uQUwOAAAAp8gsAgAAz8QCF7cgswgAAACnyCwCAADPxJxFtyCzCAAAAKfILAIAAM9EYtEtyCwCAADAKTKLAADAI8UzZ9EtCBYBAIBnIlh0C4ahAQAA4BSZRQAA4Jkoyu0WZBYBAADgFJlFAADgmUh5pd+X+fbt23Lr1i3r/vHjx2XKlCmyYsWK1OwbAAAAPDFYbNeuncydO9d8ffXqValTp45MmjTJHP/oo49Su48AAACO5yy6asP9BYtbt26Vhx9+2Hz97bffSr58+Ux2UQPIDz74ICWnBAAAwIMyZ1GHoLNly2a+1qHnxx9/XLy9veWhhx4yQSMAAIDLUWcx/WYWS5YsKUuWLJETJ07I8uXLpXnz5ub4+fPnJSgoKLX7CAAA4DhYdNWG+wsWR40aJa+88oqEhoaa+Yp169a1ZhmrVauWklMCAADgQRmGfuKJJ6RBgwZy5swZqVKlivV406ZNpX379qnZPwAAAIfiWYiSfoPFa9euiZ+fX6Isog5PZ8pE6UYAAIAMPQzdqVMn+eqrrxId//rrr81tAAAAboliXLXBKkUvx8aNG6VJkyaJjjdu3NjcBgAAgAdDisaMIyMjJSYmJtHx6Ohoc3UXAAAAl2POYvrNLNauXVv++9//Jjo+c+ZMqVGjRmr0CwAAAJ4aLL7xxhvy6aefSsOGDWXs2LFm069nzZolb731Vur3EgAAIB3XWZwxY4YpKRgQEGDKCm7atMlpWx2JHTdunJQoUcK018oyy5Yts2tz48YNefnll6Vo0aKSOXNmqVevnmzevNnuHK+++qpUqlRJAgMDpUCBAtK1a1c5ffq03Xm0T15eXnbbO++84/pgsX79+rJhwwYpXLiwWdTy/fffm5XQO3futF4GEAAAICNYuHChDBo0SEaPHm0uiazBX4sWLczFShwZOXKkfPzxxzJt2jTZs2eP9O3b15Qe3LZtm7VNr169ZOXKlTJv3jzZtWuXuQBKs2bN5NSpU9ar6eljvf766+bfxYsXy/79+6Vt27aJHk8DUy13aNkGDBiQrOfnFR8fHy/pwNNr16V1F+BG/+s6M627ADe6HTY2rbsANyrR+a+07gLc6PCXT6fZYxd9b7XLzn18yL+S3LZOnTpSq1YtmT59utmPi4szCTUNyoYNG5aovWYBR4wYIf3797ce69Chg8kgzp8/36z/0MsqL126VFq3bm1to1P9HnnkETPC64hmHnWqoF56uUiRItbMomYodUupJGcWr1+/bvf13TYAAACX83Ldpot5E8Y3kZGRiboQFRUlW7ZsMVk/C29vb7Ovo7CO6Hl0+NmWBorr1683X+si4tjY2Lu2cVYHW4eZc+TIYXdch51z585t6mO/9957Dhcpp0qwmDNnTms6VTuh+wk3y3EAAABP9vbbb0v27NntNj2W0MWLF01gly9fPrvjun/27FmH59Yh6smTJ8vBgwdNFlKHm3UYWYeIlWYV9VLK48ePN3MQ9fyacdTg09ImoYiICDOHsXPnzhIUFGQ9/tJLL5na2GvWrJE+ffqYtSVDhw51Temc1atXS65cuczX+oBI7PyaNXJu5QqJvnZNMhcqJEU6dZbAYsUcto2PjZEzPy+TSxv+kOirVyUgJEQKtn9cslesaG0TGxEhp5culavbt0n0jRuSpXBhKdyxkwSGhrrxWeF+1a9dVgb2fVSqVyou+fPllKd6TZLvVzBM52kWLPhRPvtssVy4cEXKli0mr7/eRypXLu2wbXR0jHz88TeyZMlqOXfukhQrVlBeeeU5adjwn2oR+st/2rQv5bvv1sjFi1clb95c0r59U3nhhY4mMwDPUKtssPR+tLxULJ5T8uXMIn0n/Sor/zqZ1t3KMOJTsBAlqYYPH27mIdry9/eX1DB16lTp3bu3lC1b1vy860KX7t27m4XCFjpXsUePHlKwYEHx8fGR6tWrm0BQs5gJ6WKXp556SnRm4UcffWR3m+1zqFy5srkCnwaNGvgm9fkkOVhs1KiRw69xx+XNm+Xkt99Ikae7mADx/KpVcvCDqVJh7DjxtYnwLU4tWSqXN22Uos88awLF63t2y+GZH0nZoa9Klv+fZ3B87ly5ffqUhHbvIb45csjljX/KgfcnS4UxY8WPDK7HCMziL7v2hMnchWtl4SeD07o7SIGffvpN3n77Uxk7tr9UqVJa5sz5Tnr2HCXLls2U3Lnth3vUlCnzTRD4xhsDpHjxQvLbb1vlxRffkq++miDly5cwbT75ZJF8+eVP8u67A6VkySLy99+HZPjwqZItWxbp2jXxBHWkT1n8M8m+sCvy7drD8tHghmndHaQiDaSSEkzlyZPHBHPnzp2zO677ISEhDu8THBwsS5YsMdnAS5cumTmMOrexePHi1jYaQK5bt07Cw8PNEHj+/PmlY8eOdm1sA0Wdp6iJPdusorP5lToMfezYMSlTpoy4bDW0Lu+2HTPX5eJVq1aVp59+Wq5cuSIZ0blfVkqeBg0kT/36krlAASnSpYt4+/nJpT9+d9heA7+Qlo9I9kqVxD84WIIbNTZZxXMrV5rb46Ki5Mq2rVKoQwfJVrq0BOTNKwXatDX/XljHYiBPsmLtDhk78Wv5bjnZRE81e/YSeeqpFtKhQzMT2I0d+4IEBPjLokV3fl4TWrp0jfTt+5Q0alRTChcOkaefbiWNGtWQWbOWWNts27ZXmjZ9SBo3riWFCuWTli3rS4MGVWXnzoNufGa4X+t2nJHJX++UFWQT04Zm4V21JZGfn59ZeLJq1SrrMR1a1n0dSr4bnZOomUMN3hYtWiTt2rVL1EbL4migqPHV8uXL7dpYAkUdzv7ll1/MvMR72b59u5lTmTdv3iQ/xxQFi0OGDLEuZNHl3JribNWqlRw9ejRRyjYjiIuJkVthYRJUrpz1mJe3t2QrW05uHjni9D7evr52x7x9/eTm4UPm6/i4OP1uE69M9m28fH2tbQC4XlRUtOzefUjq1atiPaa/aOvVqyrbtu13eB/9Be7nZ/+zqxmKrVv3WPerVSsnf/65Q44evVMGY9++o7Jly167oWoAnmHQoEHyySefyJw5c2Tv3r3Sr18/kxHUoWWl9Q91WNtCL42scxSPHDkiv/32m7Rs2dIEmLZzCTUw1OScxlY6p1Evs6zD1pZz6u+ZJ554Qv766y9ZsGCBmdqicyR100U3Suc4TpkyRXbs2GEeS9sNHDhQnnnmmWStMUnR5f604+XLlzdfayTcpk0bM2FS6/xo0JjRxNy8aQK7TNnsU7++Qdkk4qzjiahB5SuYbGTWUqVMZvHGvn0mkyj/X8nIJyBAAosXlzM//SgB+fOboezLmzZJ+JEj4p+MvwYA3J8rV65LbGyc5M5t/4tVh5+PHHGcTWrQoJp8/vkSqVWrohQpEiIbNuyQlSv/MOexeP75J+TmzVvyyCP9xMfH29w2cOCz0rZtY5c/J+CB4cI5i8nRsWNHuXDhgowaNcoEazraqoGeZdFLWFiY+SPTQoeftdaiBnBZs2Y1sZPOUbRdxawrmzXAPHnypFkzoqV13nzzTfH9/0ST1lv87rvvzNf6eLZ0bUnjxo3NH6m6uGXMmDFmBXaxYsVMsJjcxF6KgkVNuWoxSKVpT42YlT6ZpJTO0Q4nXH4eGxUlPn5+klEU7thRjs+bK7tHjzLpbg0Y89SrLxdthq2L9eghx+bMkV2vDtVUhpnLmKtWbbkVdjxN+w7g7kaMeF5GjpxmAkEdzSpcOL88/ngzWbToF2ubn39eL99/v04mTXrFDG3v3XvEzIu0LHQB4FlefPFFszmydu1au31d+6HFuO9Gh5d1c0brJ96rVLYuivnzzz/lfqUoWGzQoIGJSvVKLno5G61crg4cOCCFChW65/11BY5eItBWxW7dpNJzd1KrniZT1qwmmIu5YR8oR1+/Ib7Zszu8j2+2bFLyhf4SFx1tMpO6gOXU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDQDXypkzyGT+Ll2yn4996dJVyZPH8TBOrlzZ5cMPR0pkZJRcvXrDBIATJ86RwoX/Ka0xYcJsk11s3frOoogyZULl9OkLZhU1wSKQROkjsfjAS9GcRa1QnilTJvn222/NEm2dnKl+/vlnM+5+L5pW1fSq7Vb+6S7iqbwzZTJZv+t791mP6ZzDG/v2StYEq5YS3dfX987K5rhYubptq+SoYp9KVj7+/iZQjNEVUXt2O2wDwDV07mGFCiVlw4ad1mM6t0iHlqtVu/tKQn9/P8mXL7fExMTKihV/mAUtFhERkYlK5GhQmk4uqgV4BB3ZddWG+8ws6iVkfvjhh0TH33///RQvR/f0Ieh8zf4txz6fLYGhRSVLqJbO+cWsaM5dr765/ejsWeKXI4eppajCjx6RqCtXTe3EqKtX5cz335sPiXwtWljPeW33bjOHUUvrRJ4/LycXfWu+zlO/Xpo9T6SsdE6J0H/KJ4QWDpbK5YvKlas35cTpS2naNyRN9+6Pyauvvi8VK5Y0tRXnzFkqt29HmKFlNXToZBMUDh7czezv2LHf1FcsV664+XfatC9MgNmr152ff9WkSS2ZOfNrKVAg2DoMrauuO3T4d5o9T6SsdE7RkKzW/ULBgVKuaA65ejNKzly6M10LyJDBoo619+zZU5588klz6RmI5KpVS2Ju3pDT330n0devm6LcpV56yVpjMeryZbssgg4/n/5uqUReuCDe/v6mhE5ojx6SKUsWa5vY27fl1P8Wm6LdPlmySM7q1aXgY4+Jl0+K3jakkeqVi8uKr0dZ9yeMvjPHd9436+T5wVwj2xO0avWwXL58TT74YIEpyq1B4KefjrUOQ585c0G8bSba6/Cz1lo8ceKsZMkSYEroTJgwSIKC/gkqRo7sI1OnLpCxYz+SS5eumaHqjh1bSv/+ndLkOSJlKhXPJV+M+ucybyO73lnNvmjdERk68/7niuHuqF/vHl7xKRjz0ItRf/HFF2aRik6+1MDxoYf+GV5JiafXUjswI/lfV4KkjOR2mP0cZTzYSnSmpmhGcvjLp9PssYvNcF3scLQ/FyCxSNGovNbs0WsVzp4921wvumHDhqaUzsSJExNVMAcAAHhAa3JnCCmewqkLXB5//HFZunSpqQGkV295/fXXpXDhwvLYY4+ZS84AAADAs933eh8tnTN69GiZNGmSuXSMrnTW6yQ++uij8sorr6ROLwEAABLQtQCu2vCPFK2U0KFnrTSuw9B6PUK9gsuXX34pLVq0sL7Azz33nCmjo0PTAAAAyEDBohbeLlGihPTo0cMEhcHBwYnaVK5cWWrVqpUafQQAAEiEBGA6DhZXrVolDz/88F3bBAUFmWsTAgAAuALBYjqes3ivQBEAAAAZOFjU8jjPPvusFChQwKyK9vHxsdsAAABczcvbdRvucxha5ymGhYWZUjn58+dn1RAAAMADKkXB4vr16+W3336TqlWrpn6PAAAAkoBclXukKNGqhbdTcJVAAAAAZJTL/Q0bNkyOHTuW+j0CAABIAm8v121IwTB0zpw57eYmhoeHm1qLWbJkEV9fX7u2ly9fTuppAQAA8CAEi5pNBAAASC+Ys5jOgsVu3bpJbGysuXzfd999J1FRUdK0aVNzXejMmTO7tpcAAAAJECymwzmLb731lrz22muSNWtWKViwoEydOlX69+/vut4BAADAc4LFuXPnyocffijLly+XJUuWyPfffy8LFiyQuLg41/UQAADAAV1L4aoNKQwWtRB3q1atrPvNmjUzL+jp06eTcxoAAAA8iEW5Y2JiJCAgwO6YroSOjo5O7X4BAADcFZflS4fBohbi1kv9+fv7W49FRERI3759JTAw0Hps8eLFqdtLAAAApP9gUVdEJ/TMM8+kZn8AAACShKmF6TBYnD17tut6AgAAAM8OFgEAANILMovuQbAIAAA8EsGie7COCAAAAE6RWQQAAB7Jm8yiW5BZBAAAgFNkFgEAgEdizqJ7kFkEAACAU2QWAQCARyKz6B5kFgEAAOAUmUUAAOCRvFgO7RYEiwAAwCMxDO0eDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIZBbdg8wiAAAAnCKzCAAAPBKVc9yDzCIAAMB9mjFjhoSGhkpAQIDUqVNHNm3a5LRtdHS0jBs3TkqUKGHaV6lSRZYtW2bX5saNG/Lyyy9L0aJFJXPmzFKvXj3ZvHmzXZv4+HgZNWqU5M+f37Rp1qyZHDx40K7N5cuXpUuXLhIUFCQ5cuSQnj17ys2bN5P13AgWAQCAx85ZdNWWHAsXLpRBgwbJ6NGjZevWrSb4a9GihZw/f95h+5EjR8rHH38s06ZNkz179kjfvn2lffv2sm3bNmubXr16ycqVK2XevHmya9cuad68uQkGT506ZW0zYcIE+eCDD2TmzJmyceNGCQwMNI8bERFhbaOB4u7du825fvjhB/n111/l+eefT97rHK9haTrw9Np1ad0FuNH/us5M6y7AjW6HjU3rLsCNSnT+K627ADc6/OXTafbYDZaud9m517drkOS2derUkVq1asn06dPNflxcnBQuXFgGDBggw4YNS9S+QIECMmLECOnfv7/1WIcOHUx2cP78+XL79m3Jli2bLF26VFq3bm1tU6NGDXnkkUfkjTfeMFlFPc/gwYPllVdeMbdfu3ZN8uXLJ59//rl06tRJ9u7dK+XLlzcZyZo1a5o2msFs1aqVnDx50tw/KcgsAgAApFBUVJRs2bLFZP0svL29zf6GDRsc3icyMtIMP9vSQHH9+jvBb0xMjMTGxt61zdGjR+Xs2bN2j5s9e3YTuFoeV//VoWdLoKi0vfZPM5FJRbAIAAA8kiuHoTWgu379ut2mxxK6ePGiCew0o2dL9zWYc0SHiidPnmzmF2oWUoeIFy9eLGfOnDG3a1axbt26Mn78eDl9+rQ5v2YcNfiztLGc+26Pq//mzZvX7vZMmTJJrly5nPbNEYJFAACABN5++22Tqctus+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1KHmggULir+/v5mb2LlzZ7s27kKwCAAAPJKXl5fLtuHDh5s5gNdsNj2WUJ48ecTHx0fOnTtnd1z3Q0JCHPY7ODhYlixZIuHh4XL8+HHZt2+fZM2aVYoXL25toyul161bZ1Yunzhxwqyu1lXUljaWc9/tcfXfhItsdIhbV0g765sjBIsAAAAJaDZPy80E2Wx6LCHNDOrCk1WrVlmP6dCy7utQ8t3onETNHGoAt2jRImnXrl2iNrrCWUvjXLlyRZYvX25tU6xYMRPw2T6uDpXrXETL4+q/V69eNXMqLVavXm36p3Mbk4qi3AAAwCOll8v9DRo0SLp162YWktSuXVumTJlisoY6tKy6du1qgkLLMLYGdFoCp2rVqubfMWPGmABu6NCh1nNqYKjD0GXKlJFDhw7JkCFDzLC15Zya/dQ6jLoyWoe0NXh8/fXXzQrnxx57zLQpV66ctGzZUnr37m3K62hmUoe8daV0UldCK4JFAACA+9CxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZzDbUOotZaPHLkiBl+1lI2OkdRVy5bWIa9tcSNLkjR0jpvvvmm+Pr6WttocKlBqdZN1AxigwYNzOParqJesGCBCRCbNm1q+qDn0fmPyUGdRaQJ6ixmLNRZzFios5ixpGWdxcY//u6yc69tXd9l5/Y0ZBYBAIBHSi/D0A86FrgAAAAg/WcWT1z3SesuwI0YlsxYMhcZndZdgBuFdOiU1l1ABuFNZtEtyCwCAAAg/WcWAQAAkoPMonuQWQQAAIBTZBYBAIBH8vZKF9X/HnhkFgEAAOAUmUUAAOCRmLPoHgSLAADAIzE86h68zgAAAHCKzCIAAPBILHBxDzKLAAAAcIrMIgAA8EgscHEPMosAAABwiswiAADwSGS83IPXGQAAAE6RWQQAAB6JOYvuQWYRAAAATpFZBAAAHsmLOotuQbAIAAA8EsPQ7sEwNAAAAJwiswgAADwSGS/34HUGAACAU2QWAQCAR/JmgYtbkFkEAACAU2QWAQCAR2I1dDrOLM6ePVtu3bqV+r0BAACA5weLw4YNk5CQEOnZs6f88ccfqd8rAACAJAQxrtrwjxS9HqdOnZI5c+bIxYsXpXHjxlK2bFl599135ezZsyk5HQAAQIqGoV214T6DxUyZMkn79u1l6dKlcuLECendu7csWLBAihQpIm3btjXH4+LiUnJqAAAApCP3nWnNly+fNGjQQOrWrSve3t6ya9cu6datm5QoUULWrl2bOr0EAABwUDrHVRtSIVg8d+6cTJw4USpUqGCGoq9fvy4//PCDHD161AxTP/XUUyZoBAAAQAYrndOmTRtZvny5lC5d2gxBd+3aVXLlymW9PTAwUAYPHizvvfdeavYVAADAirmF6ThYzJs3r6xbt84MPTsTHBxssowAAADIQMFidHS0HDt2TPLkyXPXdl5eXlK0aNH76RsAAIBTlLhJp6+zr6+v7Ny50zW9AQAAgOcH5c8884x89tlnqd8bAACAJGI1dDqesxgTEyOzZs2SX375RWrUqGEWtNiaPHlyavUPAADAIRa4pONg8e+//5bq1aubrw8cOJDafQIAAIAnB4tr1qxJ/Z4AAAAkA5nFdDxnsUePHnLjxo1Ex8PDw81tAAAAyMDB4pw5c+T27duJjuuxuXPnpka/AAAA7hnEuGpDCoeh9ZJ+8fHxZtPMYkBAgPW22NhY+emnn0zBbgAAAGTAYDFHjhym2LZueqm/hPT42LFjU7N/AAAADlHixj28k7uwZdWqVSaz+O2338rq1aut2/r16yUsLExGjBjhut4CAACkQzNmzJDQ0FAz6lqnTh3ZtGnTXa+GN27cOClRooRpX6VKFVm2bJldGx2xff3116VYsWKSOXNm03b8+PEmBrOwJPASbu+99561jfYp4e3vvPOO6zKLjRo1Mv/qNZ+LFCliHhAAACAjr4ZeuHChDBo0SGbOnGkCxSlTpkiLFi1k//79DqfnjRw5UubPny+ffPKJlC1bVpYvXy7t27eXP/74Q6pVq2bavPvuu/LRRx+ZdSIVKlSQv/76S7p37y7Zs2eXl156ybQ5c+aM3Xl//vln6dmzp3To0MHuuAamvXv3tu5ny5YtWc8vRXM49+7dK7///rtdNF21alV5+umn5cqVKyk5JQAAgEcucJk8ebIJxjSYK1++vAkas2TJYi5g4si8efPktddek1atWknx4sWlX79+5utJkyZZ22jg2K5dO2ndurXJDj7xxBPSvHlzu4xlSEiI3bZ06VJp0qSJOactDQ5t2yW8mEpSXudkGzJkiFnsonbt2mWiaX2SmnHUrwEAADxZZGSkiXWu22x6LKGoqCjZsmWLNGvWzHrM29vb7G/YsMHpuW0XCSsdatYpfRb16tUzU/8sFz/ZsWOHuf2RRx5xeM5z587Jjz/+aDKLCemwc+7cuU3WUoeo9Up8Li/KrUGhRs5q0aJF0qZNG3nrrbdk69atJmgEAADw5GHot99+O9Gi3dGjR8uYMWPsjl28eNHML8yXL5/dcd3ft2+fw3PrELVmIxs2bGjmImpQuHjxYnMei2HDhpkAVYepfXx8zG1vvvmmdOnSxeE5dbhaM4iPP/643XEdstar7uXKlctkK4cPH26Gr5NzaeYUBYt+fn5y69Yt87VeH7pr167ma+2IJeMIAADgqTSoSjha6u/vnyrnnjp1qhm21kBQ139owKhD2LbD1l9//bUsWLBAvvjiCzNncfv27fLyyy9LgQIFpFu3bonOqffVQDJhxtL2OVSuXNnEcH369DHBcFKfT4qCxQYNGpgHr1+/vhk714mdSlOlhQoVSskpAQAAksXLhaVzNJBKSjCVJ08ek/nTYWBbuq/zAx0JDg6WJUuWSEREhFy6dMkEgJpJtJ1rqFP+9FinTp3MfqVKleT48eMmyEsYLP72229mMY0lHrsbXYCjw9DHjh2TMmXKiMvmLE6fPl0yZcpkyufoSp2CBQtaV+G0bNkyJacEAADwOH5+flKjRg0zlGwRFxdn9uvWrXvX+2oWUGMoDd50Wp8uaLHQEVyd+2hLg1I9d0KfffaZ6YOW4LkXzVDqeZNzEZUUZRa1bM4PP/yQ6Pj777+fktMBAAB4bOmcQYMGmWxfzZo1pXbt2qZ0Tnh4uBlaVjpdT4NCzQqqjRs3yqlTp0wlGf1X50FqEDh06FDrOXU9iM5R1JhLh6G3bdtm5hn26NHD7rF1+t8333xjt5LaQhfY6GPpCmmdz6j7AwcOlGeeeUZy5syZ+sGidiYoKMj69d1Y2mU07UPzS+eSBSWXv58cvh4uU3Ydlr1Xbzps6+PlJc+WKiQtC+eVPAH+cuLmbfloz1HZdOGqtU1mHx/pVbaINMyfW3L6+8qBa+Hywd9HZJ+Tc8K9Fiz4UT77bLFcuHBFypYtJq+/3kcqV058ZSMVHR0jH3/8jSxZslrOnbskxYoVlFdeeU4aNqxhbaOTl6dN+1K++26NXLx4VfLmzSXt2zeVF17oSE1TD1K/dlkZ2PdRqV6puOTPl1Oe6jVJvl/xV1p3C8n0bP1i8vy/SkpwNn/Ze/q6jFm8U3aE/fP72VYmby/p16yUdKhVREKyB8iR8zflnR/2yK/7zlvbBPpnkkGPlJUWlfJL7qz+svvUNRn3v12y84Tjc8KzdOzYUS5cuCCjRo2Ss2fPmiBQi2xbFr3oRUtss4Q6/Ky1Fo8cOSJZs2Y1i4O1nI5eKc9i2rRppij3Cy+8IOfPnzdD1TrXUB/D1ldffWUKdXfu3DlRv3QYXW/XYFRXYGuBbw0Wk1u5xivethT4XWjqU1fPaNpSn7CjDy89lR63Xc2TVA9/989ycU/0rwJ5ZES10jJp5yHZc+WGPFm8oDQpkEeeXr1FrkZFJ2rft1yoNC8ULBN2HJLjN29Jnbw55cUKxaTfbzvl4PVw02ZMjTJSPFsWmbTzsFyMjJLmhfLKU8ULyLNrtsrFiCjxZL+19exriP/0028ydOhkGTu2v1SpUlrmzPlOli1bL8uWzZTcuf/5Ybd4773PTRD4xhsDpHjxQvLbb1vlnXc+k6++miDly5cwbWbO/Fpmz14i7747UEqWLCJ//31Ihg+fKgMHPiNdu7YVT5a5yGjJKJo3riJ1a5aRbbuOyMJPBmfIYDGkw505Vp6qddUCMqlLdRn5zU7ZfvyK9GhUXFpVKSBN314ll24m/t376qPl5bEahWT419vl8Pmb0rBMXhnZrqJ0+OA32XPqmmkzrWtNKZ0/m7z+zU45dz3CtO/RqIQ0f3e1nLsWIZ7s6Pv/DJ2624i//hn6TW1v1mzqsnN7miRnFvWSfrra2XLZP9jrWKKgfB92Vn46cecvyYk7D0ndfDmldZF8suDQyUTtWxQOlrkHTsqf5+8UMV9y7KzUyJNDOpUsKOO3HhA/b29plD+PvLZpj+y4fCeTO3t/mNTPl0seCw2RT/eFufkZwpYGdU891UI6dLhTV2vs2Bdk7drNsmjRSnn++ScTtV+6dI306/eUNGpU0+w//XQr2bBhu8yatUQmThxsjm3btleaNn1IGjeuZfYLFconP/64TnbuPOjW54b7s2LtDrPBc/VqXFIWbjgu326683t2xDc7pEm5fPJknaIyc1Xin8f2NQvLjJUHZO3eO7//F/xxTOqXDpbejUvIwAVbxd/XW1pWzi/Pz9okm45cMm2mLt8vTSuEyDP1QmXSz47Lq+DeuDZ0OgsWLZf6S/g1RDJ5eUnp7Fll/sET1mP67fvXxatSIafjS+r4entLVIJJqrpfKVeQdZhahzYStomMjZXKubK75HkgaaKiomX37kPSp88T1mOaba9Xr6ps27bf6XVA/fx8Ew0PbN26x7pfrVo5+frr5XL06CkzTL1v31HZsmWvDBuWuMAqANfw9fGSioWyy4e/3CmErHT87feDF6R6UcdzvPwyeUtkjP2IWmR0rNQsntt8ncnbWzL5eJtjtiJs2gDpWYoWuFjG23fu3GnG0ROuzGnb1rOHzJIru5+vCewuR9oPN1+JjJaiWbM4vM+m81ekY/ECsuPSNTkVHiE1gnNIw5Dc4v3/w/u3Y2Nl1+Xr0q10ETl2Y79ciYySZoWCpUKuIDkVftstzwuOXblyXWJj4yR3bvsPDh1+PnIkcRZZNWhQTT7/fInUqlVRihQJkQ0bdsjKlX+Y81g8//wTcvPmLXnkkX7i4+Ntbhs48Flp27axy58TgDtyBvqbwO7iDfsrdeh+ibyO//jXuYk9G5eQTYcvyfFL4VK/VLC0qJxfvP9/9UV4ZIxsOXpZBjQvI4fO3ZSLNyKkbfVCUj00lxy/eGfaETx7gcuDLkXBok7a1JU9WrU8oaTMWdRJlgkvmRMXHSXevn6SUehClaFVSsn8f9Uwf7WevnVbfjpxzgxbW7yx9YAMr1pKlrSoLTFx8XLg2k1ZdeqCyWLCs4wY8byMHDnNBIL690Dhwvnl8cebyaJFv1jb/Pzzevn++3UyadIrZs7i3r1H5O23P7UudAGQPulClbc7VpVfhjc1c/fDLt2SbzedkCdrF7G2GbRgi0zoVE02jm0hMbFxsvvkNfl+60mpWDjxHGfggQgWBwwYIE8++aRZkZPw8jYpvYRO4U7dpWhn++XgnuJaVLQJ5nL52w8z6grmS04WolyNipHXNu8VP28vCfLzNQtWdNHL6fB/JjqfvhUhA/7YJQE+3hKYyUcuRUabRS9nbnn2ZGhPlzNnkMn8Xbp0Z76pxaVLVyVPHsfDVLlyZZcPPxwpkZFRcvXqDRMATpw4RwoX/ufnZ8KE2Sa72Lp1Q7NfpkyonD59wayiJlgE3ONKeKQJ5vJksy/GrPsXrjv+3Xs5PEr6zNpkhqNzBvqZBSu66CXs8j9ZQw0gO834XTL7+UjWgExy4XqkWfQSdonM4v0gs+geKSrKrVXJddl1SgJFyyV0rl27ZrcVfuIZ8VQx8XeyfrpAxUK/f3V/95Ubd71vVFy8CRR1jmKjArll/dnLidpExMaZQDGrr4/UzptTfjt7Z4I00obOPaxQoaRs2LDTekynYujQcrVqd6+G7+/vJ/ny5ZaYmFhZseIPs6DFIiIiMlGVAQ1Kk1iwAEAqiI6Nl79PXjMLVCz0x7JeqWDZetz+D8SEomLiTKCo05J0QcvKXWcTtbkdFWsCxaDMvtKwbF755e/EbYAHIrP4xBNPyNq1a821DFPrEjqePgS98PApea1aadl37absNaVzCpg6iTq0rLSszsWISPl473GzXz5HVsmT2V8OXrspwQH+0qNMEfEWL/nCZuV07eA7weeJ8NtSMDCzvFA+VMJu3JKfwv6p3YW00b37Y/Lqq+9LxYolTW3FOXOWyu3bEWZoWWlZHQ0KBw++c0mmHTv2m/qK5coVN/9Om/aFCTB79frngu9NmtQy5XMKFAi2DkPrqusOHf6dZs8TyReYxV9KhP5zia/QwsFSuXxRuXL1ppw4zR96nuDTtYdk0tPVTQ3EHaZ0TgnJ4ucj3268szpabzt77ba89+Nes1+1SE7Jlz1A9py+Zuos/qdFWTNf8ePV/6ycblgm2ESdWoMxNE+gDG9bQQ6fuyHf/P85kTI+ad2BDCJTSi/3p8PQei1CvVahr6/98OtLL70kGc3q0xclh5+v9CxTxBTlPnQ9XF7582+zyEXly+xvlyHy8/GW3mWLSv4sAXI7JtaU0NGSOTdtVtQF+maSPuWKmmDyRnSMrD1zUT7Ze1xiyTSluVatHpbLl6/JBx8sMEW5NQj89NOx1mHoM2cuWCe3Kx1+njJlvpw4cVayZAkwJXQmTBgkQUH/zD8dObKPTJ26QMaO/UguXbpmhqo7dmwp/ft7ds26jKZ65eKy4ut/iuZOGN3V/Dvvm3Xy/OCZadgzJNWP20+bwtmDWpaVPEH+svfUdXnu4z/l4s07c+0L5MwscTa/h7U0zuBW5aRI7ixmMYuW0Bm0YKvciIixtsmW2VeGtC4vITkC5NqtaFm247RM/GmvmcIEpHdJLsqd8BqEffv2Ndc0zJ07t93QmX6tFckzWlFuZKyi3EiejFSUG55flBueU5T7re0rXXbu16oyqnNfmcURI0aYBSrDhg1LdJFrAAAAd2CBi3ukKNKLiooy10EkUAQAAHiwpSja69atmyxcuDD1ewMAAJCMzKKrNtznMLQW3Z4wYYIsX75cKleunGiBy+TJk1NyWgAAADwIweKuXbukWrVq5uu///7b7raEdeIAAABcwYeQI/0Gi2vWrEn9ngAAACDdua8VKocOHTJD0bdv3zb7XGkCAAC4C3MW03GweOnSJWnatKmULl1aWrVqJWfOnDHHe/bsKYMHD07tPgIAAMCTgsWBAweaRS1hYWGSJUsW63Etp7Ns2bLU7B8AAIBD3l7xLttwn3MWV6xYYYafCxUqZHe8VKlScvz4nWsfAwAAuBLDxek4sxgeHm6XUbS4fPmy+Pv7p0a/AAAA4KnB4sMPPyxz5861K5cTFxdnai82adIkNfsHAADgkI8LN9znMLQGhbrA5a+//jKX/hs6dKjs3r3bZBZ///33lJwSAAAAD0pmsWLFinLgwAFp0KCBtGvXzgxLP/7447Jt2zYpUaJE6vcSAAAgAUrnpOPMosqePbuMGDEidXsDAACAByNYvHr1qmzatEnOnz9v5iva6tq1a2r0DQAAwClK3KTjYPH777+XLl26yM2bNyUoKMjuetD6NcEiAABABp6zqFdp6dGjhwkWNcN45coV66aLXAAAAFzNx8t1G+4zs3jq1Cl56aWXHNZaBAAAcAcWoqTjzGKLFi1M2RwAAAA82JKcWfzuu++sX7du3VqGDBkie/bskUqVKpnrRNtq27Zt6vYSAAAgATKL6SxYfOyxxxIdGzduXKJjusAlNjb2/nsGAAAAzwkWE5bHAQAASEtkFtPhnMXVq1dL+fLl5fr164luu3btmlSoUEF+++231OwfAAAAPCVYnDJlivTu3dvUVnR0RZc+ffrI5MmTU7N/AAAADvl4xbtsQwqDxR07dkjLli2d3t68eXPZsmVLck4JAACAB6XO4rlz5xKtfLY7WaZMcuHChdToFwAAQOrX/4NrX+eCBQvK33//7fT2nTt3Sv78+ZPfCwAAAHh+sNiqVSt5/fXXJSIiItFtt2/fltGjR8ujjz6amv0DAABwuhraVRtSOAw9cuRIWbx4sZQuXVpefPFFKVOmjDm+b98+mTFjhqmvOGLEiOScEgAAIEUI6tJhsJgvXz75448/pF+/fjJ8+HCJj4+3FuLWSwBqwKhtAAAAkAGDRVW0aFH56aef5MqVK3Lo0CETMJYqVUpy5szpmh4CAAA4QImbdBosWmhwWKtWrdTtDQAAAB6MYBEAACAtMWfRPShRBAAAAKfILAIAAI9EZtE9yCwCAADcpxkzZkhoaKgEBARInTp1ZNOmTU7bRkdHy7hx46REiRKmfZUqVWTZsmV2bbQcoda2LlasmGTOnNm0HT9+vLUSjXruuedMRRrbLeFlmS9fvixdunSRoKAgyZEjh/Ts2VNu3ryZrOdGZhEAAHik9JJZXLhwoQwaNEhmzpxpAsUpU6aYkoL79++XvHnzOqxbPX/+fPnkk0+kbNmysnz5cmnfvr0pT1itWjXT5t1335WPPvpI5syZIxUqVJC//vpLunfvLtmzZ5eXXnrJei4NDmfPnm3d9/f3t3ssDRTPnDkjK1euNEGqnuP555+XL774IsnPzyveNkRNQw9/tz6tuwA3+q1t4h8ePLgyFxmd1l2AG4V06JTWXYAbHX2/XZo99rKTP7vs3C0LPZLktnXq1DEVYqZPn2724+LipHDhwjJgwAAZNmxYovYFChQwFzHp37+/9ViHDh1MBlGDSKVXxNPa1Z999pnTNppZvHr1qixZssRhv/bu3Svly5eXzZs3S82aNc0xzWDqFflOnjxp+pEUDEMDAAAkEBkZKdevX7fb9FhCUVFRsmXLFmnWrJn1mLe3t9nfsGGD03Pr8LMtDQLXr/8ncVavXj1ZtWqVHDhwwOzv2LHD3P7II/ZB7Nq1a032Uq+qpxdNuXTpkvU2fXwderYEikr7pf3buHFjkl8LgkUAAOCRvL3iXba9/fbbZsg3u82mxxK6ePGimV+Y8Ap2un/27FmH/dYh6smTJ8vBgwdNFlKHiPVyyjpcbKEZyU6dOplhal9fXzM8/fLLL5thZdsh6Llz55qgUoet161bZ4JJ7Y/Sx084DJ4pUybJlSuX0745wpxFAACABPSyxjoP0VbC+YApNXXqVOndu7cJBHVRii5e0bmEs2bNsrb5+uuvZcGCBWZuoc5Z3L59uwkWdei4W7dupo0GkxaVKlWSypUrm3NptrFp06aSWggWAQCAR3Ll8KgGhkkJDvPkySM+Pj5y7tw5u+O6HxIS4vA+wcHBZp5hRESEGTbWAFAzicWLF7e2GTJkiDW7aAkGjx8/brKblmAxIb2/9kcvx6zBoj7++fPn7drExMSYFdLO+uYIw9AAAAAp5OfnJzVq1DBDwRY6tKz7devWvet9dd5iwYIFTQC3aNEiadfun8VCt27dMnMLbWlQqud2RhetaPCZP39+s6+PrwtgdE6lxerVq805dFFOUpFZBAAAHim9lM4ZNGiQyfbpQpLatWub0jnh4eFmaFl17drVBIWWOY+6uOTUqVNStWpV8++YMWNMADd06FDrOdu0aSNvvvmmFClSxAxDb9u2zcxz7NGjh7ldayWOHTvWrJDWLOHhw4fN/UuWLGnmRKpy5cqZeY065K1lfbR0zosvvmiylUldCa0IFgEAAO5Dx44d5cKFCzJq1CizcESDQC1RY1n0EhYWZpcl1OFnrbV45MgRyZo1qyllM2/ePLNy2WLatGmmKPcLL7xghpI1uOvTp495DEuWcefOnaYOo2YP9fbmzZubwt22w+c671EDRB2W1j5ocPnBBx8k6/lRZxFpgjqLGQt1FjMW6ixmLGlZZ3HdmZ9cdu5G+Vu57NyehswiAADwSFriBq7HAhcAAAA4RWYRAAB4pPSywOVBR2YRAAAATpFZBAAAHonMonuQWQQAAED6L51Tqvlnad0FuFFc7sxp3QW4UVxIYFp3AW50dtFXad0FuNHtsC/T7LE3nv/RZeeuk7e1y87tacgsAgAAwCnmLAIAAI/kxZxFtyBYBAAAHolY0T0YhgYAAIBTZBYBAIBHYhjaPcgsAgAAwCkyiwAAwCOR8XIPXmcAAAA4RWYRAAB4JC+vdHFdkQcemUUAAAA4RWYRAAB4JBZDuwfBIgAA8EiUznEPhqEBAADgFJlFAADgkUgsugeZRQAAADhFZhEAAHgkb1KLbkFmEQAAAE6RWQQAAB6JxKJ7kFkEAACAU2QWAQCAR6LOonsQLAIAAI9ErOgeDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIFOV2DzKLAAAAcIrMIgAA8EgkFt2DzCIAAACcIrMIAAA8kpdXfFp3IUMgWAQAAB6JYWj3YBgaAAAATpFZBAAAHolrQ7sHmUUAAAA4RWYRAAB4JDJe6fh1Dg0NlXHjxklYWFjq9wgAAACeHSy+/PLLsnjxYilevLj8+9//lq+++koiIyNTv3cAAAB3mbPoqg2pECxu375dNm3aJOXKlZMBAwZI/vz55cUXX5StW7em5JQAAAB40Ib7q1evLh988IGcPn1aRo8eLZ9++qnUqlVLqlatKrNmzZL4eIplAgAA1/By4ZZcM2bMMNP0AgICpE6dOiah5kx0dLSZzleiRAnTvkqVKrJs2TK7NrGxsfL6669LsWLFJHPmzKbt+PHjrbGVnuPVV1+VSpUqSWBgoBQoUEC6du1qYjJb2icvLy+77Z133nHfAhft6P/+9z+ZPXu2rFy5Uh566CHp2bOnnDx5Ul577TX55Zdf5IsvvrifhwAAAHAovQwXL1y4UAYNGiQzZ840geKUKVOkRYsWsn//fsmbN2+i9iNHjpT58+fLJ598ImXLlpXly5dL+/bt5Y8//pBq1aqZNu+++6589NFHMmfOHKlQoYL89ddf0r17d8mePbu89NJLcuvWLTOaqwGlBptXrlyR//znP9K2bVvT1pYGpr1797buZ8uWLVnPzys+Bek/7ZwGiF9++aV4e3ubSLZXr17mCVv8/fffJst4+/btJJ2zVPPPktsNeLC43JnTugtwo7iQwLTuAtzo7KKv0roLcKPbYV+m2WOfCP/eZecuHNgmyW3r1KljYp7p06eb/bi4OClcuLCZpjds2LBE7TULOGLECOnfv7/1WIcOHUwGUYNI9eijj0q+fPnks88+c9omoc2bN0vt2rXl+PHjUqRIEWtmUacP6ubWYWh9QQ4ePGgi3lOnTsnEiRPtAkWladNOnTqluGMAAADpfRg6KipKtmzZIs2aNbMe00Sa7m/YsMHhfXRRsA4/29IgcP369db9evXqyapVq+TAgQNmf8eOHeb2Rx55xGlfrl27ZoaZc+TIYXdch51z585tspbvvfeexMTEuH4Y+siRI1K0aNG7ttHxc80+AgAAeBoN6BJWevH39zebrYsXL5r5hZoFtKX7+/btc3huHaKePHmyNGzY0MxF1KBQq8zoeSw0I3n9+nWTjPPx8TG3vfnmm9KlSxeH54yIiDBzGDt37ixBQUHW4zpkrWtMcuXKZYa5hw8fLmfOnDGP79LM4r0CRQAAAFfz9nLd9vbbb5v5gdltNj2WGqZOnSqlSpUygaCfn5+pJqPzETUjafH111/LggULzNoPnf6ncxd1JFf/dbSG5KmnnjKLX3TU15bOpWzcuLFUrlxZ+vbtK5MmTZJp06Ylq+RhijKLOXPmNGnOhPSYplVLliwpzz33nHniAAAAnkYzcBpo2UqYVVR58uQxmb9z587ZHdf9kJAQcSQ4OFiWLFlisoGXLl0ycxg1k6j1qy2GDBlijlmm9OmqZ52LqAFrt27dEgWKetvq1avtsorO5lfqMPSxY8ekTJky4rLM4qhRo0z027p1axk7dqzZ9Gs9ppM1S5cuLf369TOrfAAAADxtzqIGhhp4BdlsjoJFzQzWqFHDDCVb6AIX3a9bt+5d+68JtoIFC5rgbdGiRdKuXTvrbbra2TbTqDQo1XMnDBR1HYlWoNF5ifeidbL1vI5WaadqZlEnWL7xxhsmnWnr448/lhUrVpgnrOlOrcFou1QbAADgQTNo0CCT7atZs6ZZjaylc8LDw60jrFo1RoNCyzD2xo0bzQJhrUut/44ZM8YEgUOHDrWes02bNmaOoq5q1tI527ZtM/MMe/ToYQ0Un3jiCTNE/cMPP5g5jWfPnjW36fxEDWJ1gY0+VpMmTUy5HN0fOHCgPPPMM2aU2KXBotYD0vo/CTVt2lQGDx5svm7VqpXD5eIAAACpwcsrfVz8o2PHjnLhwgUz8qoBmwaBWmTbsuglLCzMLkuow89aa1EXDGfNmtXETPPmzbNbxazzCrWG4gsvvCDnz583Q9V9+vQxj6E0yPzuu+/M1/p4ttasWWPmKWomVC/JrMGozlHUSjUaLCYcXndJnUWNcvXBdLP1/vvvm01flJ07d0rz5s2tUe69UGcxY6HOYsZCncWMhTqLGUta1lk8e/tOsOQKIZnbuuzcniZFmUWNdHVOokaumm61FIL86aefTPVypVd0adSoUer2FgAA4P+lkwu4PPBSFCzqPMTy5cubSuVaF0jpipp169aZIpLKMhwNAADwIF/u70GX4mtD169f32wAAAB4cKU4WNRVN1ojaO/evWZfV+roxat1WTfurlalEOn1ZCWpUCq35MsdKP3G/CK//HE8rbsFF6lVNlh6P1peKhbPKflyZpG+k36VlX+dTOtuIZmerV9Mnv9XSQnO5i97T1+XMYt3yo6wqw7bZvL2kn7NSkmHWkUkJHuAHDl/U975YY/8uu+8tU2gfyYZ9EhZaVEpv+TO6i+7T12Tcf/bJTtPOD4n0qf6tcvKwL6PSvVKxSV/vpzyVK9J8v2Kv9K6WxkGiUX3SFGdxUOHDkm5cuXMUnAdhtZNl2FrwHj48OHU7+UDJnNAJtl35LKMne74mpF4sGTxzyT7wq7ImFl8gHiq1lULyIjHKsjU5fvl0UnrZO/pazKnT13JndXPYfvBrcrJ03VDTUD573dXy4I/jsnH3WtL+YLZrW3e6VhVGpQJlkELtkrL99bIb/vPy7x+9SRfdvvrxSJ9C8ziL7v2hMnLI2eldVeA9JVZ1OsM6rUM//zzT1PLR2kFcg0Y9bYff/wxtfv5QPl180mzIWNYt+OM2eC5ejUuKQs3HJdvN4WZ/RHf7JAm5fLJk3WKysxVBxO1b1+zsMxYeUDW7r2TSdRgsX7pYOnduIQMXLBV/H29pWXl/PL8rE2y6cgl00YD0aYVQuSZeqEy6WfH15NF+rNi7Q6zwYMyXnBPsKgLWWwDRaVVw9955x3mMQJ4oPj6eEnFQtnlw18OWI9pwbHfD16Q6kUdF7X1y+QtkTGxdscio2OlZvE7V1fI5O0tmXy8zTFbETZtAMCjg3It8njjxo1Ex2/evGkqhgPAgyJnoL8J7C7eiLQ7rvvBQY6HjHVuYs/GJSQ0T6BZrdmgdLC0qJxfgoPuXCosPDJGthy9LAOal5G8QQHi7SXyWI1CUj00l9kHkDT68+WqDfcZLD766KPy/PPPm0vIaE1v3TTTqJf/00Uu96JVxK9fv263xcdFp6QrAJDu6EKVYxfC5ZfhTeXAe21kbIfK8u2mExL/zyVdZdCCLWZy/saxLWT/e23kuYeLy/dbT0pc8q+TAADpbxhar/ms10DUC2T7+vqaY3oRbA0Up06des/767URx44da3csZ/E2krvEPxfQBoD04Ep4pMTExkmebHeygha6f+F6hMP7XA6Pkj6zNpnh6JyBfnLuWoS8+mh5Cbscbm0TdumWdJrxu2T285GsAZnkwvVImda1poRd+qcNgHshBZhug0W9duHSpUvl4MGDsm/fnYnYujq6ZMmSSbr/8OHDE12XsPrjX6SkKwDgUtGx8fL3yWtmgcrKv+9cvlSHqOqVCpa564/e9b5RMXEmUNRSOrqg5cftpxO1uR0Va7agzL7SsGxeeef73S57LsCDxotgMX3XWVSlSpUyW0rmPOpmy8v7ToYyI8gSkEmKFgiy7hcKySrliueSqzci5cwFsgoPYumcoiFZrfuFggOlXNEccvVmlJy5dCtN+4ak+XTtIZn0dHVTA3HH8SvSo1EJyeLnI99uvLM6Wm87e+22vPfjnbqzVYvkNCVw9py+Zuos/qdFWfH29pKPV/+zcrphmWATdWoNRp3bOLxtBTl87oZ88//nhOeUzikRGmLdDy0cLJXLF5UrV2/KidN3VroDGSZYTJgJvJvJkyentD8ZQsXSeWTBxNbW/RF9HzL/Ll5xQF6d+Fsa9gyuUKl4LvliVDPr/siuNcy/i9YdkaEz/0zDniGpNCOohbMHtSwreYL8Ze+p6/Lcx3/KxZt3Fr0UyJnZbq6hlsbRWotFcmcxi1m0hI7WU7wREWNtky2zrwxpXV5CcgTItVvRsmzHaZn4016JiWPOoiepXrm4rPh6lHV/wuiu5t9536yT5wfPTMOeZQxeXhTPcQeveF2dkgRNmjRJ2gm9vGT16tXJ7kip5p8l+z7wXHG5M6d1F+BGcSGBad0FuNHZRV+ldRfgRrfDvkyzx74a9ZPLzp3Dr5XLzv3AZhbXrFnj2p4AAAAkC3MW3eG+87cnT540GwAAAB48KQoW4+LiZNy4cZI9e3YpWrSo2XSF9Pjx481tAAAA7lgN7ar/cJ+roUeMGCGfffaZ3eX91q9fL2PGjJGIiAh58803U3JaAAAAPAjB4pw5c+TTTz+1u1pL5cqVpWDBgvLCCy8QLAIAADcgA5hug8XLly9L2bJlEx3XY3obAACAq1E6xz1S9CpXqVJFpk+fnui4HtPbAAAAkIEzixMmTJDWrVvLL7/8Yq4PrTZs2CAnTpyQn35yXc0jAACAfzAMnW4zi40aNZIDBw5I+/bt5erVq2Z7/PHHZf/+/fLwww+nfi8BAADgWdeGLlCgAAtZAABAmqHETToLFnfu3CkVK1YUb29v8/Xd6MpoAAAAZKBgsWrVqnL27FnJmzev+VqvAe3ostJ6PDY2NrX7CQAAYIfMYjoLFo8ePSrBwcHWrwEAAPDgS3KwqJf0s8iaNavkzp3bfK0roD/55BO5ffu2KdLNAhcAAOAe1FlMd6/yrl27JDQ01AxFawHu7du3S61ateT999+X//73v9KkSRNZsmSJ63oLAABgM/XNVRtSGCwOHTpUKlWqJL/++qs0btxYHn30UVNv8dq1a3LlyhXp06ePuV40AAAAMmDpnM2bN8vq1avName9UotmE/Va0LpCWg0YMEAeeughV/UVAADABhnAdJdZ1Os+h4SEWOctBgYGSs6cOa2369c3btxI/V4CAADAM4pyJxzHZ1wfAACkBUrnpNNg8bnnnhN/f3/zdUREhPTt29dkGFVkZGTq9xAAAACeESx269bNbv+ZZ55J1KZr16733ysAAIB7onROugsWZ8+e7bqeAAAAwPOHoQEAANID5iy6B8EiAADwSCyydQ8G+wEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeCQvcl5uwasMAAAAp8gsAgAAD8WcRXcgswgAAACnCBYBAIDH1ll01ZZcM2bMkNDQUAkICJA6derIpk2bnLaNjo6WcePGSYkSJUz7KlWqyLJly+zaxMbGyuuvvy7FihWTzJkzm7bjx4+X+Ph4axv9etSoUZI/f37TplmzZnLw4EG781y+fFm6dOkiQUFBkiNHDunZs6fcvHkzWc+NYBEAAHgoLxduSbdw4UIZNGiQjB49WrZu3WqCvxYtWsj58+cdth85cqR8/PHHMm3aNNmzZ4/07dtX2rdvL9u2bbO2effdd+Wjjz6S6dOny969e83+hAkTzH0sdP+DDz6QmTNnysaNGyUwMNA8bkREhLWNBoq7d++WlStXyg8//CC//vqrPP/888l7leNtQ9Q0VKr5Z2ndBbhRXO7Mad0FuFFcSGBadwFudHbRV2ndBbjR7bAv0+yxo+K2uOzcft41kty2Tp06UqtWLRPYqbi4OClcuLAMGDBAhg0blqh9gQIFZMSIEdK/f3/rsQ4dOpjs4Pz5883+o48+Kvny5ZPPPvvMYRsN3/Q8gwcPlldeecXcfu3aNXOfzz//XDp16mSCzPLly8vmzZulZs2apo1mMFu1aiUnT540908KMosAAMBjS+e4aouMjJTr16/bbXosoaioKNmyZYsZArbw9vY2+xs2bHDYbz2PDj/b0iBw/fr11v169erJqlWr5MCBA2Z/x44d5vZHHnnE7B89elTOnj1r97jZs2c3gavlcfVfHXq2BIpK22v/NBOZVASLAAAACbz99tsm+Mpus+mxhC5evGjmF2pGz5buazDniA4VT5482cwv1CykDhEvXrxYzpw5Y22jGUnNDpYtW1Z8fX2lWrVq8vLLL5thZWU5990eV//Nmzev3e2ZMmWSXLlyOe2bI5TOAQAAHsp1pXOGDx9u5iHa8vf3l9QwdepU6d27twkEdTGNLl7p3r27zJo1y9rm66+/lgULFsgXX3whFSpUkO3bt5tgUYeOu3XrJu5EsAgAAJCABoZJCQ7z5MkjPj4+cu7cObvjuh8SEuLwPsHBwbJkyRKzEOXSpUsmANRMYvHixa1thgwZYs0uqkqVKsnx48dNdlODRcu59XF0NbTt41atWtV8rW0SLrKJiYkxK6Sd9c0RhqEBAIBH8nLhf0nl5+cnNWrUMPMLLXRoWffr1q171/vqvMWCBQuaAG7RokXSrl076223bt0ycwttaVCq51ZaUkcDPtvH1XmVOhfR8rj679WrV82cSovVq1ebc+jcxqQiswgAAHAfBg0aZLJ9upCkdu3aMmXKFAkPDzdDy6pr164mKLTMedSA7tSpUyYDqP+OGTPGBHBDhw61nrNNmzby5ptvSpEiRcwwtJbV0XmOPXr0MLfr8LUOS7/xxhtSqlQpEzxqXUbNUj722GOmTbly5aRly5ZmyFvL62h9xxdffNFkK5O6EloRLAIAAI+UkuLZrtCxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZZQh1+1lqLR44ckaxZs5pSNvPmzTMrly20nqIGfy+88IIZStbgrk+fPuYxLDS41KBU6yZqBrFBgwbmcW1XWuu8Rw0QmzZtavqg5Xe0NmNyUGcRaYI6ixkLdRYzFuosZixpWWcxNv5vl53bx6uiy87taZizCAAAAKcYhgYAAB4pOQtRkHJkFgEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeKT0UjrnQUdmEQAAAE6RWQQAAB6KnJc7ECwCAACPROkc9yAkBwAAQPq/3F9GFBkZaS4qPnz4cPH390/r7sDFeL8zFt7vjIX3Gw8ygsU0dP36dcmePbtcu3ZNgoKC0ro7cDHe74yF9ztj4f3Gg4xhaAAAADhFsAgAAACnCBYBAADgFMFiGtJJ0KNHj2YydAbB+52x8H5nLLzfeJCxwAUAAABOkVkEAACAUwSLAAAAcIpgEQAAAE4RLHqA5557Th577LG07kaG9/nnn0uOHDlcdv61a9eKl5eXXL161WWPgX/oa71kyRK3Py7vc/p07Ngx875s3749yfdp3LixvPzyyy7tF5AeECymQiCnv2D69u2b6Lb+/fub27SNq35ZwTXvp25+fn5SsmRJGTdunMTExLj8sevVqydnzpwxV4HA/Tt79qwMGDBAihcvblaoFi5cWNq0aSOrVq1K034l530msHTNz7ZuuXPnlpYtW8rOnTvN7fr9oe9LxYoV07qrQLpDsJgK9JfMV199Jbdv37Yei4iIkC+++EKKFCmSpn1D8ukHiH5oHDx4UAYPHixjxoyR9957z+WPq8FpSEiI+SDD/dE/vGrUqCGrV682792uXbtk2bJl0qRJE/NHXFpyxfscFRWVaufKCD/buukfDZkyZZJHH33U3Obj42PeFz0GwB7BYiqoXr26CRgXL15sPaZfa6BYrVo16zH9sGrQoIEZytS/avWX1OHDh623FytWzPyr99EPEh3isDVx4kTJnz+/ua9+4EVHR7vl+WU0moXSD42iRYtKv379pFmzZvLdd99Zb1++fLmUK1dOsmbNav3wUb/++qv4+vqajJYtHaZ6+OGHzdfHjx832a2cOXNKYGCgVKhQQX766SenWaTff//dfB9kyZLF3KdFixZy5coVc9u3334rlSpVksyZM5vvCe1neHi4W16j9O6FF14wr+WmTZukQ4cOUrp0afNaDxo0SP78809ru4sXL0r79u3N61uqVCm791n9/fff8sgjj5j3Ol++fPLss8+a+1joe6PZS32P9f3RNp988ol5H7p37y7ZsmUz2emff/7Zep+E77Oz7wkNeDW4VXqb7SiFPu6LL75oHjdPnjzm+6JHjx7WwMdCf0fkzZtXPvvsMxe90p75s61b1apVZdiwYXLixAm5cOGCw5GddevWSe3atc399Hevtr/bKIP+bHbt2tW8X/o9pd87+kenLf3+0M8LvV2/9yZPnmyd3qJ98Pb2lr/++svuPlOmTDG/j+Li4lL9NQGSgmAxlegv6tmzZ1v3Z82aZT4sbOkHiH5Y6S8C/atWfynoLwvLLwD9YFO//PKLCUBsg881a9aYwFL/nTNnjpk/pxtcT4MxS+bm1q1bJmifN2+eCQ7DwsLklVdeMbc1bNjQDHnqbbYf1gsWLDDfH0qD/MjISHNfzXa9++67JhBxRD+0mjZtKuXLl5cNGzbI+vXrTVARGxtrvj86d+5szrt3714TgDz++ONC2VSRy5cvmz/M9LXW4Csh23mnY8eOlaeeesoMRbZq1Uq6dOli7q80mPvXv/5l/njTn1k957lz50x7W/rzqAGb/vxq4Kh/YDz55JNmuHnr1q3SvHlzE2Tq944jzr4nNKBYtGiRabN//37znk+dOtXucTVLqX9QzJw5U3r16mX6aPnjRf3www/mcTt27JgKr+yD5ebNmzJ//nwTzOsfWwmdOnXKfE/UqlVLduzYIR999JEJut944w2n59RgXr9X9I8O/ZnVn0c9h+UPe32vdMrSf/7zH/Pz/e9//1vefPNN6/1DQ0PNH322nyVK9/Xc+pkBpAktyo2U69atW3y7du3iz58/H+/v7x9/7NgxswUEBMRfuHDB3KZtHNHb9S3YtWuX2T969KjZ37ZtW6LHKFq0aHxMTIz12JNPPhnfsWNHFz+7jPt+qri4uPiVK1ea9/WVV16Jnz17tnl/Dh06ZG0/Y8aM+Hz58ln333333fhy5cpZ9xctWhSfNWvW+Js3b5r9SpUqxY8ZM8bhY69Zs8ac/8qVK2a/c+fO8fXr13fYdsuWLaatfq/B3saNG81rs3jx4ru20zYjR4607ut7pMd+/vlnsz9+/Pj45s2b293nxIkTps3+/fvNfqNGjeIbNGhgvV1/RgMDA+OfffZZ67EzZ86Y+2zYsMHh+5yc7wkLfdxq1aolal++fHnzPWjRpk2b+Oeee+6ur0NG+tn28fEx749u+rrmz5/f/Cw5+v372muvxZcpU8b8HrD9edef59jYWOv78J///Md8feDAAXP/33//3dr+4sWL8ZkzZ47/+uuvzb7+zm7durVdv7p06RKfPXt26/7ChQvjc+bMGR8REWH2tX9eXl6mf0Ba4c+UVBIcHCytW7c22T79K1C/1myDLR2O0GyQZp+CgoLMX5FKs1P3okNTOqfGQodEzp8/74JnAs3GaGYnICDADCNpVkbnLSodOipRooTT90H/+j906JB1qFO/HzQTZclwvfTSSyYzUb9+fXNpMMvk+rtlFh2pUqWKuU2HoTWLpUNbluHpjC452dXKlStbv9b3SH8uLe+nZpM0k6/fC5atbNmy5jbb6SO259CfUc1S6ftioUPTytnPa3K+J2zpnMyENLtoyUppFlSHvy1ZbYgZ1tefK900E6zD9/ozrlMBEtKMfd26de3mlup7pBnJkydPOmyv8x3r1KljPabfC2XKlDG3WTLEOqxtK+G+Vr7Q76P//e9/1t8h2m/L5wWQFggWU5H+UtYfbB0ecvQLWocQdYhLP9g3btxotqROTte5cLb0FxjzV1z7gaLBvS5a0vfTEuw5eh9sgxOdH6bvs35gO/qw1g/zI0eOmGFJHXKsWbOmTJs2zenwtzP6YbJy5Upzfh2m1nPoh9LRo0clo9O5h/q+7Nu3775+rjQo0PfSElxYNv2+0CkHdzuH7TFLsOHs5zU53xO2HA2x63w5PZcOgeoQq86DtsyXxZ3XTIedddPh5U8//dRMD9LfyemFTi3Q91F/h+hngy6UJOBHWiNYTEW62EF/uHV+iv7FauvSpUvmr8qRI0eajJAukEiYCdJfEkrnpCHtP1B0gVJKVkbqh//ChQvlv//9r8lCajbCls5F03lLOidVV1s7+6DSjNXdyrxoEKLn1nl327ZtM98/lmxERpYrVy7z8zdjxgyHC36SWoZGF67t3r3bZHQsAYZlcxSo3Q9n3xPJ/Z2gmSzNTGmgoX+4Jpw3jcQ/QzoP0LaShYX+jrbMO7TQOYe6aKlQoUIO2+viF0sSwPb3vv5Bp/QPus2bN9vdL+G+5XeIzl3/8MMPzTl1PjKQlggWU5Fme3S4Yc+ePXZDxkpXx+kvcg0gdJhSS3roYhdbmpXSbJJlIv21a9fc/AyQGjRQ0eFMHVpM+GGtq1d1NbVmAHXxgw5z6oeMI8OHDzcfJLqyV4cmNVOmk+x1Na5+IL311ltmMr1OY9AgQ1d0OjtXRqOBogZYOsSni0Q0G6g/mx988IEZWkwKXXiiIwE6dUTfBx161vdO39PU/IPubt8TugJWAxqdGqHvr2Y770UDDc2G6/Pt1q1bqvXzQaALibRagW76+uiCJEsGOSH9udOV0tpGf/aWLl1qpgno721HC000o92uXTvp3bu3WYym0xieeeYZKViwoDmu9Fy60l1XQOv35Mcff2xGBxKWUdL3/6GHHpJXX33VfP/dbZQBcAeCxVSmQYJuCekvF63FuGXLFlP0deDAgYlq92kWSz/M9BdIgQIFrL9g4Fn0vda5ixpQ6HCSLT2mQYh+GGgmWku6aPbAEb1txYoV5kNHgx4NcvQDS79P9HtMV8/qSkttpxnrSZMmmflXEDMvWAMvnVKgmTr9mdOVp5qp1YA7KfRnUDNJ+p7pimadh6iBna6mTs1VqXf7ntBAQzPHWrJF5z5quZx70dW0OpdW/2jR54B/6B/i+tropnML9Y+Ab775JlGZMstrr4Gdzm3UOcKa+e3Zs6f5WXNGM7o6l1RLGOnPq2Yl9RyWaQk6EqAr1zVY1HNqf/SzQOdHJ6SPpSNVDEEjPfDSVS5p3QngQaO/6DUTlLBuH+BqminTQEcDF4Yv0z/NRGrm8rfffrM7Pn78eBPIJnXBE+BKlKoHUpFOHdBFCjopnUAR7qQLaHSKgmaYNfvZtm3btO4SHNA6rZrl1nmvOgStUwZsRxc02Nfi3NOnT79rTUfAnQgWgVSkUwd02EqHrPQDAXAXnbuqq5918YUubuGydemT/n6YMGGC3Lhxw0yX0KlHOs/UQqcafPnll2ahEkPQSC8YhgYAAIBTLHABAACAUwSLAAAAcIpgEQAAAE4RLAIAAMApgkUAAAA4RbAIAAAApwgWAQAA4BTBIgAAAJwiWAQAAIA483+Df+ge33lzowAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "data = {\n", + " 'Math': [80, 85, 90, 95, 100],\n", + " 'Physics': [82, 88, 91, 97, 99],\n", + " 'Chemistry': [78, 83, 88, 90, 94],\n", + " 'Biology': [76, 81, 85, 89, 92]\n", + "}\n", + "\n", + "df = pd.DataFrame(data)\n", + "corr = df.corr()\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(corr, annot=True, cmap='YlGnBu')\n", + "plt.title(\"Pearson Correlation Matrix\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "da3c9cb7", + "metadata": {}, + "source": [ + "## 🔍 Pearson Correlation Analysis\n", + "\n", + "This notebook enhances the data visualization module by demonstrating how to analyze the relationship between multiple features using Pearson correlation.\n", + "\n", + "Pearson correlation measures the strength and direction of a **linear relationship** between two continuous variables. It returns a value between -1 and 1:\n", + "- **+1** → Perfect positive relationship \n", + "- **0** → No linear relationship \n", + "- **-1** → Perfect negative relationship\n", + "\n", + "Here, we calculated the correlation matrix for student scores in Math, Physics, Chemistry, and Biology. The resulting **heatmap** helps us visually understand how strongly each subject score is related to the others.\n", + "\n", + "Such correlation analysis is crucial in **feature engineering and preprocessing**, especially before building machine learning models. It helps identify:\n", + "- Redundant features\n", + "- Strong predictors\n", + "- Potential multicollinearity\n", + "\n", + "This is a helpful addition to meaningful visualizations when analyzing real-world datasets.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}