Clustering_ML.ipynb

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    "### K-Means Clustering Algorithm\n",
    "\n",
    "#### K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. In this topic, we will learn what is K-means clustering algorithm, how the algorithm works, along with the Python implementation of k-means clustering.\n",
    "\n",
    "### What is K-Means Algorithm?\n",
    "\n",
    "#### K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.\n",
    "\n",
    "#### It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way that each dataset belongs only one group that has similar properties.\n",
    "\n",
    "#### It allows us to cluster the data into different groups and a convenient way to discover the categories of groups in the unlabeled dataset on its own without the need for any training.\n",
    "\n",
    "#### It is a centroid-based algorithm, where each cluster is associated with a centroid. The main aim of this algorithm is to minimize the sum of distances between the data point and their corresponding clusters.\n",
    "\n",
    "\n",
    "#### The algorithm takes the unlabeled dataset as input, divides the dataset into k-number of clusters, and repeats the process until it does not find the best clusters. The value of k should be predetermined in this algorithm.\n",
    "\n",
    "### The k-means clustering algorithm mainly performs two tasks:\n",
    "\n",
    "#### Determines the best value for K center points or centroids by an iterative process.\n",
    "\n",
    "#### Assigns each data point to its closest k-center. Those data points which are near to the particular k-center, create a cluster.\n"
   ]
  },
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"
    }
   },
   "cell_type": "markdown",
   "id": "810ed7e7",
   "metadata": {},
   "source": [
    "### Hence each cluster has datapoints with some commonalities, and it is away from other clusters.\n",
    "\n",
    "#### The below diagram explains the working of the K-means Clustering Algorithm:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bdb685a",
   "metadata": {},
   "source": [
    "### How does the K-Means Algorithm Work?\n",
    "\n",
    "#### The working of the K-Means algorithm is explained in the below steps:\n",
    "\n",
    "Step-1: Select the number K to decide the number of clusters.\n",
    "\n",
    "Step-2: Select random K points or centroids. (It can be other from the input dataset).\n",
    "\n",
    "Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters.\n",
    "\n",
    "Step-4: Calculate the variance and place a new centroid of each cluster.\n",
    "\n",
    "Step-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster.\n",
    "\n",
    "Step-6: If any reassignment occurs, then go to step-4 else go to FINISH.\n",
    "\n",
    "Step-7: The model is ready."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "075b0067",
   "metadata": {},
   "source": [
    "K-Means clustering\n",
    "\n",
    "K-means is a centroid-based clustering algorithm, where we calculate the distance between each data point and a centroid to assign it to a cluster. The goal is to identify the K number of groups in the dataset. \n",
    "\n",
    "“K-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.” – Source\n",
    "\n",
    "It is an iterative process of assigning each data point to the groups and slowly data points get clustered based on similar features. The objective is to minimize the sum of distances between the data points and the cluster centroid, to identify the correct group each data point should belong to. \n",
    "\n",
    "Here, we divide a data space into K clusters and assign a mean value to each. The data points are placed in the clusters closest to the mean value of that cluster. There are several distance metrics available that can be used to calculate the distance. "
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
    "image-3.png": {
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"
    },
    "image-4.png": {
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"
    },
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"
    },
    "image-7.png": {
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"
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1pXN8+kwv18Tfffvb9fx90YsQ4aPO0jnDDhoFVLKSs9CnKNBrqfNFmLzroQ2ebrjr5j3rWz3kKcSKOP6ned2E3evm5Pm6E6B1oW0qo7ROpfWo7T/vd5cZV0dPVYSS9770tI1ehDxcYRuBa7ePT9Z0J+QREphBoE9Pat5PZqQXYvOGz8vlGSb3vpmny9Qm/oddETe3N5ftjt57sRSRIAKAUDXrS7ocdYw4/R/7MEP+otcp19MkkYz0iWlW5BwMPi8CvJkoh0CfnlRTpJoXXLoc/+NCqZdPn6l7QK2bbptIhV0Rh73E6X+2s6xIrbvU8jgfHlBTpJr1J+rYpBiR6nqdIjiEtg+CTjoP6xcVwzxKQyuzJNBnpaSde9oF0LYtLth62+kzg2nuC7sibrM91rEPzSVZVgCNXimBac19af3hP/Uo6lsa09zXZTovmu3oCER4+dwg9DjXrNiOGiutDia+CIE+RYqmBdq8mzNMpBdc82KMi4Z9+KQA7eeL5GOZY6LjBDaErWFXxLE+3SfW0cX4hmuvWbvdy+TZY9dHIPXUo85EHPVrUsx+dx492qlucT68L8KkdKetx/uzM8X66odnmkGgT5HiCY55xwjTLoJ0W1ysxJsQqMBDc8oku9L18Z+Xz9i8yE0kzmm8HQTiIaitB2la/6NuxTqWF/FseFiMtBaJecjE5u0oHXM5eAJ9ihSZ5QIhpJ8Q4EKJCy/i5sXDzA2b9EZ4+sQGQpuNrEuFiWUC+fR91OCr+cYNjOuCOjRvExxTb3V9cMOr55jm9dVpeUPN7hsvJA0YJgFF6my5KFLDrJ+lWKVIlVKS5mPtBPoWqcgAHQn4Kui0J7jwRpjxYQij6rGB0PQCyUPYmsa8Q7PDRJS48TQCvOchcL3RhBfXBfUp6le6Do+Ld1HM3jIt3eY2rrlFZ3yJ8xNbr5tkXd4YgVWJFBni6ZHv5tDGHTf3uCBZZj3jMobWXFbbffc9rR1AuMEQyBcDJDdWcJ44WwI8CNHrr276Swaz07xHiCZkRG2RTDaHgqTi0xTF9LqM/RZ5D7aInR4jgbkIrFKkMIDOFNz0uaHTVk7g67SEPm7yCB2B8zBo8UPHH6vwzObK/Iydookm7MbesH3GoW6WwFQC8cVmHtDotMN1iPdC6Domqnmiyw4eGM8qEcIT4hTLbXEIFr0CqfPNdF2WwEYI3HTTVVlWRprauMAjxMh+Bi9yA+BpddmLfSMF4kklsCQBmvvw1JqDiBGmEKs0jv+x3etmyQLw8H4J5NjVFG8JL6etS288IdKUwkWKWPVLzNQkMHwC0QpA010qQnF9TIp5+OujhWP4hLQwGwKb7Pa9CCQ8Jdrcm+35ky46mgIRKsVqEdoekysBrhMCzYjptRKClcbxP66VXPOs3YUSyMmTihe6xJNEqW09HheBp8tCi9FsSaCVADNHjFsdWqZIQsAIvNP1Qa4VoSs3TSAnT4qZLNra2duEqW1d1+68my4bzy+BvgjwMMr7W3oMIkjpu1vEqevUS33ZZToSmElAkZqJyB0kkD0BRSr7ItzeDOTU3Ef33Lp9vaXZos1zaq5joOOqu9xvb00y57kQwJtyAtlcSks7Rzl5Utg6z7xn8SI4RIplAgLnSHorvQQkIIGMCOTiScVnPEJ45ombYoXALStSTOBJYCwJbfmkx6fj+e87r4wq/opN/btvf7v+GgD1IupH1BcHyq4YvsmXRSAnT4qmumniFKIUcXNf1nPDWLQEubnE4GFmS49ehqTLf9ax3cGQixIu4zgGk9Ojjs4JUUeiLuLN8xFD6rJNz2WUt7lYMYFcPCkw8CQaF/s8cVOsuGEwrdEiSBngyCDitvNynvRctPmzv4MiFyGd9zFcT/RCrd+dJvPyjetN8j4VEaNO551jrZfAignkJFLY2pxlIhWIEIqI48YQ+/CyGKHpihQPapJAxTmaMfsT9Kq60s53fzyorjOQM6QC7zvfXGu5BFZMILcLBMFoCsKk5RArnmoJizy10iNw2qzSce44VyxHbJPOiivwAJLHQyfQxDfRg2rzqs6t813mAApRE4ZLICdPCorc9GkmCREIL4nlEIqIWcenD/BmCIt0u0UUeYcQ5+sac6ze1HDrfx+W4UER0nqZ1pO0Pqbr4z+zjuf2sNgHN9OQwFwEFKnpmBSp6XzcOhopUtYCCayQQI5PcDSr0DkhnkQnxTS9hBe1qDdDb8B5mnDiaTnisU2nzyzVo3CFRW/SPRGgGZnQ7Mk3rgNJU9+u+nFuG3W6J3NMRgJlEchRpCgBRAfb6fZNYAZnbgAICjeLxx95pL5xEC9TYvTQa7vZdFmXK+NluG3TsTzIENrqxCRRivV1fPqMHxncpgpjXrsRyP0GGk0tiEncLMjTsuIUFEmr7eaTrttxw0nejcU+dLyI9IzLIxCeVJR3W0wdiXrS3M76RT398miaIwk0CBw6dLk30AaTdJEZJZo3lS7L3ICWneUitcf/wyNA+RLqZuFkHNQ0UaIOxXbi3B8Wh1cqWlQMgUUHtxYDYEZGeFdAU2KbMKU3meZNJ/ZnehzfN8yAnPnmmD3i2S98cWo9iTrRjGmeXmR4RObYNF8C8xFwVoTpnPj2DjMING8s8y5/+N5jeqrTERez9YZbjuyoJ/EQk9aVWJfG1DHrSTHVwIz0TcDmvtlEecptm+mCm096s4llmn1iIlq9qNl8S9mDJjs68USdCHFqLqfr8aJsDi6lBpiPlRDwJjofVoRqUrNf3HQQJ25I0XxjE858bEvaK32gaROndB31xHdRJZW+eVkJAUVqfqzcgJhVgIAghShx4yGwTNONT8bzMy1xT64pxvEhQlE34kGGmHpy6tQp30OVWPjmqX8CitT8TBWp+Vlt856K1DaXvnnvnYBNUt2QxrgsvCXGtvDCm6dm/rOO7d1SdO8SCVAnuLaYVivqSFpP7jx6tLd68s1vfrOeX5J5IktkaZ62nICe1JZXALOfLYH77n+ofjCiCzw9UO963/07HpZ8YMq2aDU8JaAnldLwvwSGTYDOOzxYEhAp3nvxnivtkME7r3h36vU97PLUujkIWInngOQuEhgIAa5XBg1PGjicdtDgP6JmD8KBFJ5mLEZAkVqMm0dJYN0EYhxWU4hmLSNUXufrLi3P1xsBK29vKE1IAisjwBeip32VOm3uaxOt44+e6G3S5ZVl0oQl0EZAkWqj4roSCdALjs4FdDSI9zr8z6GDAXbO08QXYpWOz+J/DDAvsVzNU+EEFKnCC9js1QT4AjXNZU89/Uw9xRU3bQLTXbEutg8VF8Mb6BDR5iWFMLVtS9f5bmqopatdUwkoUlPxuLEQAiFCilQhBWo2tofAZQcPOABwe4p763LKRL88iDHxa+pVtP1nMOxQH9rwpF5OvlXVZn94VBGzT/wn9sOKW1f9y8gwlb+MnJgLCewmgOg0Z7Bvu8HHOnrCDfGamPR5+rB7VoxI0fFiNyHXSGDgBPSkBl5AmrcwATwHBrXOuoE3t9MkODSvA7GNgbtNe1lOPaZYjnUs//D0j/345sI1yQM3SmCIT40bBeLJiyFA3Z7U2aDtRj9ed/rM4Jr96IGIeI5t/NFPOv13UG8x1Xr7MqJIbV+Zb0OOuaHT3XzRm/qHjj82uKYxvqLdFN3wltridF0O3ey3oV6axwUIKFILQPOQwRNAoBCquFF3FSs6UXBjH9LNvbZnDuFFyMh3BMZX2f188FVWAycRUKQmkXF9zgSOvOOOuQa/ThKvF77yjfq91NDeTSE2fPKDkNoeYhzCFPGQeyzmXL+0fY0ELtm/b3DNGmvMvqcqlAA38a7vcOJGz82fmzvfgSIMDRECTGDsF3amTYDkAYHle1UEPaihlZ72dCagSHVG5gEZEFCkFKkMqqkmzkOA8Rfz7Oc+EsiJAB4GHwJMm8Tm/U93bb7VlEN+aY7kGqZTBV4T/6+7/s1Z2J4DX20cAAFFagCFoAkrIcAYwLopLJmpIZr04p0NwpWuY5ljhjrzxEpAmagEhkzA5r4hl462LUOAJr+u3dARrPq4u+/RG1kGvsdKoC8CelJ9kTSdIRKgCaytA8UkT+rUqVN2NhhiQWrT9hJQpLa37Lcl5wgV75iaTX/pOyq2Pf7II/W7nW3hYj4lkAUBRSqLYtLIJQnQlZwxgQyIZUZ0vnRLOHnyE3U389i25Gk8XAIS6JuAItU3UdMbOgHeOcU4Iv4P3V7tk8BWE1Cktrr4zbwEJCCBYRNQpIZdPlonAQlIYKsJKFJbXfxmXgISkMCwCShSwy4frZOABCSw1QRyEqn3X3JJPe3LpAL7syNnp4ihK/GkfVwvAQlIQAIZEchFpG7ft6c6vGdUfXo0qrsSNxH/rwcfrJg9g+1vOG9U995q7uOyBCQgAQlkRiAHkUKcEKl/Go3qgBA151YLgYp92P+mmw/rUWVWHzVXAhKQwA4CQxap4+9+d0XAMwrxifi/79lTvf6yg7X3hECxHNsivuHaa6onnnxSodpR4i5IQAISyIjAkEUqMOI1tYnQQ+edV0UIYYr42d/9XedgC4DGEpCABHIlkINIwfbKiy+s3zeFCE2L6/dTl16qB5VrpdRuCUhAAkEgF5HCXpr1/mrv3l3Neqlg/dW73rXrfVXk1VgCEpCABDIjoEhlVmCaKwEJSGCbCOQkUpQL3lTqOTX/8xntbSo/8yoBCUigaAI5iRSDdac1+dEU2OyaXnThmTkJSEACpRPIRaRisO7Md1J797YO9i29HM2fBCQggSIJ8LG3HDKGB8Ug3mbzXtty22DfHPLYt413ve/+6rWvu7KeSopyxss88o476tD3uUxPAhKQwEoI5OBJIVBt46Ro/ps0Tor9+Wz4SqANPFHECUE6/uiJ6q//8Z/Hn03nP1+lJcDmrb9521byGXjxaZ4EJJASGLInFTNOMC1S02NisC5eQoQ2EdvGGSeuu/7Ntaf03X/9cfXyj34yNXzo+GM1v7Q++L9MAne9/demPpA89fBbpm4vk4q5yoLAkEUqACI26dx9bYN10+bAF666qp5OaZvm7rvr7nsqwtvvuHuqMDWF66mnn6kQtmBtXB6Bw4cPVHfc8Z+qyw4e2FXOn/zk1RWBfa6+em914sR9u/Ypj4g5yopADiIF0DuPHq1+/bJ91bTBusxKgUCxLyGrgljC2C9/6x/q5jua8ObxoJpCdevtR7aG1RKYszwU4XnyyQuqn/70vOqll87bIVRf/eqRehnxeuWVUb0PYnXXXW+zPmRZ2oUa3fZ0NdSs4inMeofG9geOPbhVFxne02Mfe7oOTQGaZ/m5F1/a2vd3Q63ry9rFdU343Of21uKDSBFYPnTo8uqll36n3o44hUDFPnhdPrgsWwIe3xsBRao3lBtLSJHaGPrBnliRGmzRaFhXArk093XN1zbtTxnSc4/wyr9N7yzR9KzYn8BnT7aJWel5pZmP0PSk8JZo/qPFgea/8J7SON5vls7I/GVCICdPKhOkazcTkQqxaROpWBcxQpX+Z9nmnbUX21pOyPXdbM5DkNrWsZ6OEw5NWEvReJJ5CTiN0Lykhrsf3fDbPKTmurblECsG+Q43h1q2DIH6IeZcx4jUY2r+/9ynH/bd5DKgPXY1BBSp1XBdZ6o03bx8+szZ0BgbFSKUxvE/RItlu6Gvs8TWfy6a/iZ5T4gVHSm8F6y/XDzjHASsmHNAGvgub7zu+ooeegQEpylCIUbNOPaj27rvJgdeyEuaR9fy6Ire9KBYZvuSp/BwCayGgCK1Gq7rTPXkyU/Ug3jp5RfCgyDF/2acihXbOP7D9x7zJrXOQlvjuRisiyfVJk6xDi/LB5U1Foqnmp+AIjU/qyHvGdNDPfuFL47FKRWj5v8QLnoEWgeGXLLL2RYDdqc19YVQ0dvPurAcb49eAQEr5QqgbjBJypMZKFJRCkEipmmPwH8EiimnNmiup14Rgb//+wcqwqTefZO6n9NlfVsnZl5RUZjssgQUqWUJDut4RUNXPjIAABHlSURBVGpY5bEpaxSpTZH3vL0ToJmo90RNcKMEeLfANEnpPH7hPeFBEWgWdADvRotppSePGSfaPCY+5UIdaRvoS9MfHSwcN7fS4jHxLgT0pLrQymdfZg3gRsTNhk9yPPHkk/Us6ZQ3wZtQPmW5iKXMz0do9uhjsG58HQAhQ8RSIfuP/7ih3o6QLXJej5FA7wT0pHpHaoISGAwBBIkJY/GQGKzbfAcZHhcdK/7932+tGM7w+COPKFCDKUENGSlSq60EL379W9Xnn3/Ri361mE19CgE+vXHTTVdN7blHF3XvBVMgumlzBKyY/bKPT7PDlcA8aEw5xBMtzW80w/V7RlOTwGwCNPfO3ss9JDBAAr48769QEKIbbjlSz/yQdgGv/58+U3dkoEOD7wH7Y25KEpBA4QRe9yv27uujiBlbQgcFBInec7tEKplTj/FJjkXpg7ppSEACxRPwZrl8ETMdEb2h2oRpkmDFTA96VcvzNwUJSKBgAorU8oWrSC3P0BQkIAEJtBJgLEXrBlfOTQBvKB042+ZRta2jkwVh6N9y+ou//FL11NPPVPfd/1A93mpuMO4oAQlIYFkCvpNajiA37uOPntjV1BczOyBOk5r8EDbCUJv8opMHPRTJJ0JFXlnGZsIDxx70IWe5KuTREpDANAI2902jM3sbHwtsTuja5jVNWzc0T4oeigR6K5K3NpHlnRqBd3HWodn1xD0kIIEFCXiDWRDcucPwJrhZTxKhuMFH3LbfkLwRxAmviYCt0+yOvNBk6Xi75eqRR0tAAhMIKFITwMy5epZIxY084rjpR8z6oYgUzXl4T22fog970zj+kwdEzYHKc1Yad5OABOYn0JzLa/4j3RMCEwfvnhsXFTfyiEOsiFlHGMpkrwjuwk2Xp88M9t2aNVUCEsiYgJ7UcoXHdDPRNJYK0KT/qVhFxwmmS1rOiuWPpgcfgpvaF3mIdc04hDb2G4pHuDwNU5CABAZDgFmPB2NMpoYgMs0u6NzQ227qcUP/4ekf1998ogfdh+89tvEywA5mzAibmwIUdk+L+UbVnUePbjwvmVYjzZaABNoIKFJtVLqtw5uiW3b9LieZ/qh5Q0cAEDNimtVoXiN0O9tq9sYb5J1UKlJhf6xrxmyPdfwnT7zTWo2FpioBCWwlAUVq+WJXpM7OVahILV+XTEECEmgQUKQaQBZcxINgeqS2Zj+8jPCg8Dz4xtSys8/TtMY5z9+3v/bGaHKMsEgvOwbp0uSXeoOplxRe1bT4ha98YzCdQBYsRg+TgASGRsDeff2VCO+WmMGDcUOIUggWN3sC46nYZ9kxRTxYIFAIVZwjxINz0GyHHV0+AX7q1KlaZCOdNA6xasbsE+v4f/LkJwbTnb6/UjUlCUhgowT0pPrHzwwSeDgEPCYY4+X08f6JNBChVETa/iNeeHZd3hG1dQBpS7ttHWLV5Vz9UzdFCUigSAJ8VrrIjBWYKYYL4KW1icSkdTT9zds1nPFadZNf0vkjPKW2ONZxbpownQeywEpnliSwaQJ6Upsugdnnx3Mi4KFNEqNJ6/GoujQv4qnRAYIwKc3m+u+98v1O55idY/eQgAQkcI6ATTTDrwqIDKGLcKRCQu/Deb0paCBUBN55RTrhNREjfMQE3oHpQQ2/DmmhBLIlYMeJ4RddiFQIxqw4BCX2Q0i6eFNBhPdpNAF+/vkXxx1BECjEkmZEAmIW+xtLQAIS6J2AnlTvSHtNkHc9DBSeZ7BwiNKu+PSZuhPHooZRR+hUQUcQYj5PsmhaHicBCUigEwFFqhOute+sSK0duSeUgASGRECRGlJptNtCkxthl4fU6IUXzXwRp/vjAbWn7loJSEACAyagSA24cM6ZRg9MQio6Xf7zHkmRGn45a6EEJNBCQJFqgTKwVQgMgQ4QTS8plonT/4hYvXz6TN3xgXdaA8uW5khAAhKYTUCRms1o03tET7rmQNt5vCmEiqbCTefB80tAAhJYiIAitRC2jRxEd2+8qRCnpuc09p7Ci/rRTyrm5PPDlhspLk8qAQn0QUCR6oPi+tJAcFKhCsFqi+kZ6Dim9ZWNZ5KABFZAQJFaAdQVJxmzQdAhIvWe4j/rmWVCgVpxQZi8BCSwegKK1OoZr+IMdIRAhHhfxbeg+PQ7gfdPdLJY9yfpmUKJ+QVpXlxFfk1TAhLYUgKKVJ4Fr0jlWW5aLQEJdCRgz6+OwNy9JhAzszO5LO/J+HYVk9gSs2xnDSuKBCTQCwE9qV4wblUieHHUG8Kkmdmfe/Gleo6/6D6/VYDMrAQk0B8BPan+WG5DSgjTffc/NO4G39arcLzu9Jnas8K74ltY28DHPEpAAj0T0JPqGWjBydExAy9qLELJ3IGz1lnPCq4YZk0CqySgJ7VKumWlTW/CecdoNUWLZsFFvmlVFkFzIwEJdCagSHVGtpUH/OEff3ahz9czK0YEvLCthGemJSCBxQkoUouz26Yj73rf/RVC1fSQJi3HlE2xnWUGGBO2iZt5lYAEliSgSC0JcEsOxwviM/IhOovEDPilt9+WIDObEpBAHwQUqT4olp8G9QSRmSVO4UGlcfz/1LPP1WOpyqdlDiUggd4IKFK9oSw6IUWq6OI1cxIYLgFFarhlMyTL6H4+7XtW4S1N87RIY0h50hYJSCADAopUBoU0EBOZ6miaCLEtxCqN+U94/WUHFamBlKVmSCAbAs4EkE1RbdxQ5uljuqM2oQpRatvGOjpdOKB340WoARLIj4CeVH5ltkmLZw3oDbEi5rtW33vl+3Xw21abLDXPLYGMCehJZVx4GzKdZj9mkEgnlw1xCk+KZbbjfRE2ZKqnlYAEciegSOVegpuxH8+IwCDfWqxOn6mbAfGeWOaji3pPmykbzyqBoggoUkUV50Yyg2d1/r79tSgR+y2pjRSDJ5VAmQTuPHrUppgyi9ZcSUACEsifgCKVfxmaAwlIQALFEuCDdMVmzoxJQAISkEDeBO66622KVN5FqPUSkIAEyiVgc1+5ZWvOJCABCWRPwE8nZF+EZkACEpBAuQQUqXLL1pxJQAISyJ6AIpV9EZoBCUhAAuUSUKTKLVtzJgEJSCB7AopU9kVoBiQgAQmUS0CRKrdszZkEJCCB7AkoUtkXoRmQgAQkUC4BRarcsjVnEpCABLInoEhlX4RmQAISkEC5BBSpcsvWnElAAhLIngAfp8s+E2ZAAhKQgATKJKAnVWa5misJSEACRRB44NiDelJFlKSZkIAEJFAggfvuf0iRKrBczZIEJCCBIgjoSRVRjGZCAhKQQJkEFKkyy9VcSUACEiiCgCJVRDGaCQlIQAJlElCkyixXcyUBCUigCAKKVBHFaCYkIAEJlElAkSqzXM2VBCQggSIIKFJFFKOZkIAEJFAmAUWqzHI1VxKQgASKIKBIFVGMZkICEpBAmQQUqTLL1VxJQAISKIKAIlVEMZoJCUhAAmUSUKTKLFdzJQEJSKAIAopUEcVoJiQgAQmUSeDxRx5xFvQyi9ZcSUACEsifgJ5U/mVoDiQgAQkUS+CJJ5/Ukyq2dM2YBCQggcwJ2NyXeQFqvgQkIIGSCehJlVy65k0CEpBA5gQUqcwLUPMlIAEJlExAkSq5dM2bBCQggcwJKFKZF6DmS0ACEiiZgCJVcumaNwlIQAKZE1CkMi9AzZeABCRQMgFFquTSNW8SkIAEMiegSGVegJovAQlIoGQCilTJpWveJCABCWROQJHKvAA1XwISkEDJBBSpkkvXvElAAhLInIAilXkBar4EJCCBkgkoUiWXrnmTgAQkkDmBkyc/4ac6Mi9DzZeABCRQLAE9qWKL1oxJQAISKIMAQqVYlVGW5kICEpDA2gmEiKwivunmw1WEVaRvmmcfAIbMYe0V2hNKQAISmJfArbcfqSLMe4z7lUVgyAKqbcN/yJlVRmVdLeZmIwSikm3k5J5UAhKQgAQkMI2AIjWNjtskIAEJSGCjBBSpjeL35BKQgAQkMI2AIjWNjtskIAEJSGCjBBSpjeL35BKQgAQkMI2AIjWNjtskIAEJSGCjBBSpjeL35BKQgAQkMI2AIjWNjtu2ncB3PvLa6vx9++cKo71Xb2wuzOdu31ONLnj/Quc/+qYrq1f9wsV1HolvuOXIQukMpa5QXkOxRTt6IKBI9QDRJLaKwBvOG1U/f817e70R3ji6sSIsChKR2nPRQ3Mff+LQedX5Bw7W4dfe8dEdxx07fEUtWPtv+9Md6xe1bd3HKVLrJr7i8ylSKwZs8sUROLxnVI3e+ef93sD3HK5GhAV/XT0pbuQ3nfyXOrSd8msfeG0tYG3bhr5OkRp6CXW0T5HqCMzdt57AvCL14G1vrC47eGDcVHjRL19f3f7wZ3YIEeLCTTUNzWa7SIemuNdceXMd3nvypV3pNI+bVlB4UdO2t21LvTVsumT/vlZPrHns77/zxh0cyMN7nnhhx/lJ+8Lrznp0T7zn5opw5cUXzkz/8FWvqdmRZjCZJFKfefRdFfvDsYRmzSbnYpcfOPZgRSg2g2ZMAj0TuH3fnpme1KQmQW68o+s+vuN6e/4Dl1f1+6yWd1qkc/mtJ8b7P/N7N1cEBOJXH/rb8fqunhTHk26a9ixM9Tn2Xl1dd/2bx+flmKOX76n2nP9bdWimMY3Dhb/x6XE6z/+3UTX6xbMTXjfTuPOSUTW66M7xvmwPUU/tP3H0DdUN115Ti1Yzjdr2X9zpqbL/Jt8jNm10eQIBPakJYFwtgQkEZnlSz7xl766bappU65M+AtUiUkfecceOm3OkU58j6SjBTbjLO6mXHr72Z97bgYN1Z4nHPvZ067ninJyj1fZENG75k9PjNGphaIhLpEWcpoVIpcvpfvW+ief30V8a1Xlty2/9rq2l4wQPFgcf+Juxbc30XR4wAUVqwIWjaYMkoEjtLhYEhqBI7WbjmiUJ2Ny3JEAP3zoCs5r7ZokY23/ut7+286m+Y8eJL9/z6h0dLWqvJfGsuhQK72oOHbp8h2fV7PFHevU5JnTugAlh/3u/NM7XPJyiyRJPiibDSXanXhb8OE96rvS4dN9YX3tfV9w2Mf3Yz3iABBSpARaKJg2awCwR4j3MFR/5zsQb4rtHo2pX9+4pIsV7lroTQbODRSIYy4hUEzadFnhn1Wx+nHYO8tTMFxzCw5oURy/JaWljXyo8wXcS43TfNG+8O6vtOHCwahPhdF//D4iAIjWgwtCULAjM8hC4iU57/1F7HM0xSC0ixY2bXnjNHoFAanoes27yi4AlH3h84fVNOwd5auaL43eJ8QRD6o4TUzzBVHgWFan01AxgroW40Zki3cf/AyGgSA2kIDQjGwKzPKnmzbqZMY5/1e98Y6en1SJS7BcC0UwDwUibx+rlDoN5m+m1LZMPeuBFL7xapBLvLT0GW5v2zuKQHt9FpDhP1+a+9Fzp/1lppfv6f0ME7DixIfCeNlsC3HyjmaotE/S8SwWkuU/qFYy3tfTum+aRsS0d/DvNyxmf49wfesClxza3xzKeBh5heIWco9X2pHdfHEtc90CcIGps/9Dxx8ZC3UWkpvXuY1ubjR++99j4XKmNH7lytEOI023+l4AEJJAlAZ6+p4kUmUJEmlMn0UGBG/++w5/adcPkxkp40/H/Od5Wi0mjOYp59QhPPfyWuikwBrB2ESnsq9/PTJgWiYG62NK0vz7H3qvrsUjpYNxaMOlq3tLdnG17Wjos1GOrEs+vi0hhf/BKx0kxndNNNx9uFSnGWl188dvGbEnjj+492w2f//4kIAEJFENglicVGR3PynBOgNpmWoh9H3/rf64ITXGINJghofmy/+6rX10LFdMbdRUpzotoEng/M54Z49yYqd/79Fd33NDZn3PE2KTxLBJzdEIYzx5xjkPbzBtdRSq4/fpl+2pmsL3/s9+tbYZhbE9j7IhejM44kZLxvwQkIIECCCwihAVk2yxIQAISkEAOBBSpHEpJGyUgAQlsKQFFaksL3mxLQAISyIGAIpVDKWmjBCQggS0loEhtacGbbQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIGEwP8Hwy37Hr3HNJsAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "613e349d",
   "metadata": {},
   "source": [
    "### How does K-means work?\n",
    "\n",
    "Let’s take an example to understand how K-means work step by step. The algorithm can be broken down into 4-5 steps. \n",
    "\n",
    "1. Choosing the number of clusters \n",
    "The first step is to define the K number of clusters in which we will group the data. Let’s select K=3.\n",
    "\n",
    "2. Initializing centroids\n",
    "Centroid is the center of a cluster but initially, the exact center of data points will be unknown so, we select random data points and define them as centroids for each cluster. We will initialize 3 centroids in the dataset.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "\n",
    "3. Assign data points to the nearest cluster\n",
    "Now that centroids are initialized, the next step is to assign data points Xn to their closest cluster centroid Ck\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "In this step, we will first calculate the distance between data point X and centroid C using Euclidean Distance metric.\n",
    "\n",
    "![image-3.png](attachment:image-3.png)\n",
    "\n",
    "And then choose the cluster for data points where the distance between the data point and the centroid is minimum. \n",
    "\n",
    "![image-4.png](attachment:image-4.png)\n",
    "\n",
    "\n",
    "4. Re-initialize centroids \n",
    "Next, we will re-initialize the centroids by calculating the average of all data points of that cluster.\n",
    "\n",
    "![image-5.png](attachment:image-5.png)\n",
    "\n",
    "![image-6.png](attachment:image-6.png)\n",
    "\n",
    "\n",
    "5. Repeat steps 3 and 4\n",
    "We will keep repeating steps 3 and 4 until we have optimal centroids and the assignments of data points to correct clusters are not changing anymore.\n",
    "\n",
    "![image-7.png](attachment:image-7.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b49a8b",
   "metadata": {},
   "source": [
    "### The optimal centroids are found and the assignments of data points to the correct clusters no longer change, typically indicate convergence of the algorithm. In KMeans, convergence occurs when the algorithm reaches a stable state, and further iterations don't alter the centroids or the assignments significantly.\n",
    "\n",
    "### Here are two common indicators that can suggest convergence in KMeans clustering:\n",
    "\n",
    "#### 1. Centroid Stability: The centroids of the clusters no longer change significantly between iterations. The algorithm repeatedly updates the centroids based on the mean of the data points assigned to each cluster. When the centroids stabilize and their positions remain nearly unchanged across iterations, it suggests convergence.\n",
    "\n",
    "#### 2. Data Point Assignments: Data points stop switching clusters or change minimally between iterations. As the algorithm progresses, data points are reassigned to the nearest centroid (cluster). When these assignments remain consistent over subsequent iterations, it indicates that the data points are now relatively well-clustered, and further iterations don’t alter these assignments significantly.\n",
    "\n",
    "### To explicitly identify convergence in the KMeans algorithm:\n",
    "\n",
    "#### You can set a maximum number of iterations (max_iter) in the KMeans function. If the algorithm reaches this limit without significant changes in centroids or assignments, it might be considered converged.\n",
    "\n",
    "#### Monitor changes in the inertia (or within-cluster sum of squares), which is available via the kmeans.inertia_ attribute in scikit-learn. When the inertia stabilizes or changes very little between iterations, it suggests convergence.\n",
    "\n",
    "#### Keep in mind that convergence doesn’t guarantee the optimal clustering for a particular dataset. Sometimes, running KMeans multiple times with different initializations (using the n_init parameter) and choosing the best result based on some criterion (like inertia or silhouette score) might yield more stable and reliable clustering results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e0141a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries    \n",
    "import numpy as nm    \n",
    "import matplotlib.pyplot as mtp    \n",
    "import pandas as pd    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "807a7e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset  \n",
    "dataset = pd.read_csv('C:/Users/Faraz Ahmad/Downloads/Mall_Customers.csv')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ad0e59f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:, [3, 4]].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43a5f6aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans  \n",
    "import matplotlib.pyplot as plt  # Importing matplotlib with alias plt\n",
    "\n",
    "wcss_list = []  # Initializing the list for the values of WCSS  \n",
    "#Initializes an empty list wcss_list that will store the Within-Cluster Sum of Squares (WCSS) values.\n",
    "\n",
    "# Using for loop for iterations from 1 to 10.  \n",
    "for i in range(1, 11):  \n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42, n_init=10)  \n",
    "    kmeans.fit(x)  \n",
    "    wcss_list.append(kmeans.inertia_)  #  retrieves the inertia (WCSS) for the fitted KMeans model and appends it to the wcss_list.\n",
    "\n",
    "plt.plot(range(1, 11), wcss_list)  # Changed mtp to plt for consistency\n",
    "plt.title('The Elbow Method Graph')  \n",
    "plt.xlabel('Number of clusters (k)')  \n",
    "plt.ylabel('WCSS')  # Corrected ylabel to WCSS for clarity\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "aad58075",
   "metadata": {},
   "source": [
    "\n",
    "The Within-Cluster Sum of Squares (WCSS), also known as inertia in the context of KMeans clustering, is a measure that quantifies the compactness or homogeneity of clusters created by a clustering algorithm.\n",
    "\n",
    "In KMeans clustering:\n",
    "\n",
    "WCSS measures the sum of squared distances between each data point in a cluster and its centroid.\n",
    "For each cluster, it computes the sum of the squared distances of each point in the cluster to the centroid of that cluster.\n",
    "The total WCSS is the sum of these values across all clusters.\n",
    "The goal of KMeans clustering is to minimize the WCSS. A lower WCSS value indicates that the data points within each cluster are closer to their centroid, suggesting more compact and well-separated clusters.\n",
    "\n",
    "When using the Elbow Method to determine the optimal number of clusters, practitioners plot the number of clusters against the corresponding WCSS values. The \"elbow\" point in the plot represents a point of diminishing returns where adding more clusters doesn't significantly decrease the WCSS. This point often serves as a heuristic for determining an appropriate number of clusters for the data.\n",
    "\n",
    "As the number of clusters increases, the WCSS tends to decrease because each cluster tends to have fewer data points, resulting in smaller within-cluster distances. However, beyond a certain point, the rate of decrease in WCSS slows down, leading to an \"elbow\" in the plot, suggesting an optimal number of clusters for the given dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f68a69fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#training the K-means model on a dataset  \n",
    "kmeans = KMeans(n_clusters=5, init='k-means++', random_state= 42)  \n",
    "y_predict= kmeans.fit_predict(x)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4119a4f",
   "metadata": {},
   "source": [
    "n_clusters=5: Specifies the number of clusters (in this case, 5) that the algorithm will attempt to identify within the dataset.\n",
    "\n",
    "init='k-means++': Indicates the method used for initializing the centroids before clustering. \n",
    "\n",
    "'k-means++' is a smart initialization technique that aims to place centroids in a way that speeds up the convergence of the algorithm.\n",
    "\n",
    "random_state=42: Sets the random seed for reproducibility. It ensures that if you run the code multiple times, you'll get the same results as long as the other parameters and data remain unchanged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7cc33d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visulaizing the clusters  \n",
    "mtp.scatter(x[y_predict == 0, 0], x[y_predict == 0, 1], s = 100, c = 'blue', label = 'Cluster 1') #for first cluster  \n",
    "mtp.scatter(x[y_predict == 1, 0], x[y_predict == 1, 1], s = 100, c = 'green', label = 'Cluster 2') #for second cluster  \n",
    "mtp.scatter(x[y_predict== 2, 0], x[y_predict == 2, 1], s = 100, c = 'red', label = 'Cluster 3') #for third cluster  \n",
    "mtp.scatter(x[y_predict == 3, 0], x[y_predict == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4') #for fourth cluster  \n",
    "mtp.scatter(x[y_predict == 4, 0], x[y_predict == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5') #for fifth cluster  \n",
    "mtp.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroid')   \n",
    "mtp.title('Clusters of customers')  \n",
    "mtp.xlabel('Annual Income (k$)')  \n",
    "mtp.ylabel('Spending Score (1-100)')  \n",
    "mtp.legend()  \n",
    "mtp.show()  \n",
    "\n",
    "#  x[y_predict == 0, 0] selects elements from the first feature (dimension) of the dataset x where the corresponding cluster label in y_predict is equal to 0.\n",
    "#  x[y_predict == 0, 1] selects elements from the second feature (dimension) of the dataset x where the corresponding cluster label in y_predict is equal to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7867a777",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WCSS: 68268.79713978159\n"
     ]
    }
   ],
   "source": [
    "kmeans.fit(x)\n",
    "print(\"WCSS:\", kmeans.inertia_)\n",
    "\n",
    "#In KMeans clustering, inertia_ attribute provides the sum of squared distances of samples to their closest cluster center. \n",
    "#Lower inertia indicates tighter clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a67cc0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score: 0.4497782204673568\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "silhouette_avg = silhouette_score(x, y_predict)\n",
    "print(\"Silhouette Score:\", silhouette_avg)\n",
    "\n",
    "#Measures how well-separated clusters are. It ranges from -1 to 1, where higher values indicate better-defined clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c5cdc9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calinski-Harabasz Score: 478.48790740519104\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import calinski_harabasz_score\n",
    "ch_score = calinski_harabasz_score(x, y_predict)\n",
    "print(\"Calinski-Harabasz Score:\", ch_score)\n",
    "#Computes the ratio of between-cluster dispersion to within-cluster dispersion. \n",
    "#Higher values represent better-defined clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c7c5aa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Davies-Bouldin Score: 0.7890863394750914\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import davies_bouldin_score\n",
    "db_score = davies_bouldin_score(x, y_predict)\n",
    "print(\"Davies-Bouldin Score:\", db_score)\n",
    "\n",
    "#Computes the average similarity between each cluster and its most similar one. Lower values indicate better clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72fabd1a",
   "metadata": {},
   "source": [
    "Remember, the choice of evaluation metric depends on the specific characteristics of your data and the goals of your analysis. It's often recommended to use multiple metrics to comprehensively assess the quality of clustering results.\n",
    "\n",
    "These metrics can help in determining how well the clusters are formed, how separated they are, and how internally consistent they are. However, interpreting these metrics might vary depending on the nature of the dataset and the problem domain. It's important to consider domain knowledge and the context in which the clustering is applied while interpreting these evaluation scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "34aa053f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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SggMHDmDgwIFo1KgRunfvjgMHDiAqKgrZ2dl1rs5gzvulOiEhIXj22Wfx7LPPAgCOHj2KYcOG4aWXXsJTTz1V7WOLi4vRt29feHt7Y8WKFWjTpg0UCgWuXLmCv/3tbwZ9UCgU8Pb2NuhrWVmZ7vatW7eMvu6hoaEG28aNG4eDBw9i8eLF6NGjh64U3vDhw40+/6rvE+15NrZvY9tMWbFiBd5++2289957GDp0qG779evXAQDz58/H/PnzjT5W++/ErVu36tyP6mi/KDRp0gSAJq9+8ODBuHbtGhYvXoxOnTrB19cXarUavXr1Muv9Y+n5JzIXg1iie5KTkyEIApKTk6FWq7F9+3bUq2f6IzJ58mS8++67+OWXX7B9+3aT7YKCguDj42NyEo02H++rr75CSUkJdu3apRcQVy5hZKl69eohMTERiYmJ+Ouvv3DgwAH8/e9/x5AhQ3DlyhUoFAqjjxsyZAj27duHPXv2YOzYsbU6tpeXl9G8UG1AoOXr66vLN7x+/bpuVHbEiBE4e/ZstccICgqCIAj4/vvvjc6krrpN6lq22sBQG4RUZmyCkTEPPfQQFi9ejKNHj+LgwYNYunSpbvu+ffsQGRmpu+1I+vXrh8GDB+Orr75Cfn4+goODTbY9dOgQrl27hsOHD+tG3wDUqRRTo0aNkJ6ebrC96uteWFiIb775BkuXLkVSUpJue3l5OQoKCozuu+r7RHuejZ3TvLy8GifwAZpJY4sXL8ayZcsMJoBq/w14+eWX8be//c3o47V5t40aNTLZDyloq6toJzqePn0ap06dwrZt2zBx4kRdu/Pnz5u9T2ucfyKA1QmI9CxbtgzJycnYuXMnxo0bh7t375psGxcXh8mTJ+Oxxx7DY489ZrLdI488ggsXLqBRo0aIjY01+NH+AdT+4awceImiiHfeeUeS59agQQOMGTMGs2fPRkFBQbVF0adMmYLQ0FAsXLgQV69eNdpm165d1R6vRYsWeoXwAc0fs+LiYpOPCQkJwaRJk/DUU0/h3LlzKC0tBWA4Aqr1yCOPQBRFXL161ehra6rChFTatm2L0NBQgxnkOTk5OHbsmFn76NmzJ/z9/fHGG28gLy9PV5d40KBB+Pnnn7Fz505ER0frRsZMMfUa1dX169eNltFSqVT4/fffoVAo0KBBg2r7YOy9DQCbN2+udb/i4+Nx+/Ztg5J2VSc4CYIAURQNjv3uu++alToEAL169YK3t7fBl9Vjx46ZlY6RlpaGadOmYfLkybovKZW1bdsWrVu3xqlTp4y+j2NjY1G/fn3d8z548KDeFyeVSqVXTaC2Tp06hX/84x9o0aKFLkXGknNny/NPBHAklsjAkiVL4ObmhsWLF0MURezYscPkiOx7771X4/7mzJmDL7/8Ev369cPcuXPRuXNnqNVq5OTkYN++fZg3bx4eeOABJCQkwNPTE0899RQWLlyIsrIypKSk4M8//6z1cxkxYgQ6duyI2NhYNG7cGJcvX8Ybb7yBiIiIai/VBwQE4Ouvv8YjjzyCbt264fnnn0dcXBw8PT3x+++/4+OPP8apU6dMjhoBwDPPPIPFixdjyZIl6N+/P7KysvDWW2/pZvVrPfDAA3jkkUfQuXNnNGzYEGfOnMFHH32EuLg43UixNhhds2YNhg0bBnd3d3Tu3Bl9+vTBc889h2effRYnT55Ev3794Ovri9zcXF2ZtJkzZ9b69auJm5sbkpOTMX36dIwZMwaTJ0/GX3/9heTkZISFhdVY/QDQ5DP2798fe/bsQWRkJFq2bAkA6NOnD7y8vHDw4EG8+OKLNe5H+xpt2LABEydOhIeHB9q2basLfmrro48+wubNmzFu3Dj06NEDAQEB+OOPP/Duu+/qqmx4enpW24fevXujYcOGmDFjBpYuXQoPDw9s37691kX1AU21hH/+85+YMGECVq5cidatWyM1NRV79+7Va+fv749+/frhtddeQ1BQEFq0aIEjR47gvffe0wXfNWnYsCHmz5+PFStWYOrUqXj88cdx5coVLFu2rMbL+NnZ2Xj88ccRFRWFZ5991qBEW7du3eDl5YXNmzdj2LBhGDJkCCZNmoSmTZuioKAAZ86cQUZGBj7//HMAwCuvvILdu3dj4MCBWLJkCRQKBTZu3GhxqaqffvoJAQEBUCqVuHbtGg4ePIiPPvoIwcHB2LNnj+6ctmvXDi1btkRSUhJEUURgYCD27NmjV2JQy5bnnwgAqxOQa9PORq5aOUAURXHlypUiAPFvf/ubWFFRUeOsXq2q1QlEURSLi4vFV155RWzbtq3o6ekpBgQEiJ06dRLnzp2rN8t4z549YpcuXURvb2+xadOm4oIFC8T//Oc/IgDxu+++07Xr37+/2KFDB4NjT5w4UW9G/Ouvvy727t1bDAoKEj09PcXmzZuLU6ZMES9dumTW65OXlycuWrRI7NChg6hQKEQvLy+xVatW4vTp08Vff/1V185YdYLy8nJx4cKFYnh4uOjj4yP2799fzMzMNKhOkJSUJMbGxooNGzYUvby8xKioKHHu3LnizZs39fY1depUsXHjxqIgCAYzoN9//33xgQceEH19fUUfHx+xZcuW4oQJE8STJ0/W+Jpp7zM2w/y1114zaAsjVRe2bNkitmrVSvT09BTbtGkjvv/+++Kjjz4qduvWrbqXV2fDhg0iAHHatGl62xMSEkQA4u7du/W2m5oB//LLL4tNmjQR3dzc9N4zERER4sMPP1zj8zYmKytLnDdvnhgbGys2btxYrFevntiwYUOxf//+4kcffWTQ3lQfjh07JsbFxYkKhUJs3LixOHXqVDEjI8PgeUycOFH09fU12K+x99gff/whjh49WvTz8xPr168vjh49Wjx27JjBPrXtGjZsKNavX18cOnSoePr0aYP3YnX/HqjVanHVqlVieHi46OnpKXbu3Fncs2ePyfeO9vjfffedrqqDsZ/K7+NTp06JTzzxhBgcHCx6eHiIoaGh4sCBA8W3335bry///e9/xV69eoleXl5iaGiouGDBAnHLli0WVSfQ/nh5eYlhYWHi4MGDxQ0bNohFRUUGj8nKyhITEhLE+vXriw0bNhQff/xxMScnx+hnoa7nn8gSgigamYpIRES19tdff6FNmzYYNWoUtmzZYu/uEBE5JaYTEBHVQV5eHlauXIn4+Hg0atQIly9fxj//+U/cvn0bL730kr27R0TktBjEEhHVgZeXFy5duoRZs2ahoKAACoUCvXr1wttvv40OHTrYu3tERE6L6QREREREJDsssUVEREREssMgloiIiIhkh0EsEREREcmOS03sUqvVuHbtGurXry/58pNEREREVHeiKOL27dto0qRJtYvGuFQQe+3aNYSHh9u7G0RERERUgytXrqBZs2Ym73epIFa7/OKVK1fg7+8vyT6VSiX27duHwYMHw8PDQ5J9knXwXMkHz5U88DzJB8+VPPA8aRQVFSE8PLzGZbNdKojVphD4+/tLGsQqFAr4+/u79BtODniu5IPnSh54nuSD50oeeJ701ZT6yYldRERERCQ7DGKJiIiISHYYxBIRERGR7LhUTiwRERHJlyiKuHv3LlQqlb27YhVKpRL16tVDWVmZ0z5HAHB3d0e9evXqXO6UQSwRERE5vIqKCuTm5qK0tNTeXbEaURQRGhqKK1euOH09e4VCgbCwMHh6etZ6HwxiiYiIyKGp1WpkZ2fD3d0dTZo0gaenp1MGeWq1GsXFxfDz86u2yL+ciaKIiooK3LhxA9nZ2WjdunWtnyuDWCIiInJoFRUVUKvVCA8Ph0KhsHd3rEatVqOiogLe3t5OG8QCgI+PDzw8PHD58mXd860N532FiIiIyKk4c2DnaqQ4l3w3EBEREZHsMIglIiIiItlhEEtERERkZ4Ig4KuvvrJ3N2SFQSwRERGRFeXl5eGFF15AVFQUvLy8EB4ejhEjRuDgwYNWOd7hw4chCAL++usvq+wfAFauXInevXtDoVCgQYMGVjtOdVidgIiIiFyGSi0iPbsA+bfLEFzfGz0jA+HuZr1yXZcuXUKfPn3QoEEDrF27Fp07d4ZSqcTevXsxe/ZsnD171mrHritRFKFSqVCvnmG4WFFRgccffxxxcXF477337NA7jsQSEbkUlVrEjxdu4evMq/jxwi2o1KK9u0RkM2mnc/HgmkN46p3jeOnTTDz1znE8uOYQ0k7nWu2Ys2bNgiAISE9Px5gxY9CmTRt06NABiYmJOH78uNHHGBtJzczMhCAIuHTpEgDg8uXLGDFiBBo2bAhfX1906NABqampuHTpEuLj4wEADRs2hCAImDRpEgBNULp27VpERUXBx8cHXbp0wRdffGFw3L179yI2NhZeXl74/vvvjfYxOTkZc+fORadOner+ItUSR2KJiFxE2ulcJO/JQm5hmW5bWIA3lo6IxtCOYXbsGZH1pZ3OxcyPM1D1a1teYRlmfpyBlPExkn8OCgoKkJaWhpUrV8LX19fg/rpchp89ezYqKipw9OhR+Pr6IisrC35+fggPD8eXX36J0aNH49y5c/D394ePjw8A4JVXXsGuXbuQkpKC1q1b4+jRoxg/fjwaN26M/v376/a9cOFCrFu3DlFRUXZLFTAHg1giIhdgjz/gRI5CpRaRvCfL4P0PACIAAUDyniwkRIdKmlpw/vx5iKKIdu3aSbZPrZycHIwePVo3EhoVFaW7LzAwEAAQHBysC0JLSkqwfv16HDp0CHFxcbrH/PDDD9i8ebNeELt8+XIkJCRI3mepMYglInJy9voDTuQo0rML9K5AVCUCyC0sQ3p2AeJaNpLsuKKo+dRZY4ncF198ETNnzsS+ffswaNAgjB49Gp07dzbZPisrC2VlZQbBaUVFBbp166a3LTY2VvL+WgNzYomInJwlf8CJnFH+bdPv/9q0M1fr1q0hCALOnDlj0eO0q1lpg2AAUCqVem2mTp2Kixcv4plnnsGvv/6K2NhYvPnmmyb3qVarAQDffvstMjMzdT9ZWVl6ebEAjKY+OCIGsURETs5ef8CJHEVwfW9J25krMDAQQ4YMwcaNG1FSUmJwv6kSWI0bNwYA5Oben3CWmZlp0C48PBwzZszArl27MG/ePLzzzjsAAE9PTwCASqXStY2OjoaXlxdycnLQqlUrvZ/w8PDaPkW7YhBLROTk7PUHnMhR9IwMRFiAN0xd1BegmeTYMzJQ8mNv2rQJKpUKPXv2xJdffonff/8dZ86cwb/+9S9dbmpV2sBy2bJl+O233/Dtt9/i9ddf12szZ84c7N27F9nZ2cjIyMChQ4fQvn17AEBERAQEQcA333yDGzduoLi4GPXr18f8+fMxd+5cfPDBB7hw4QJ+/vlnbNy4ER988IHFzysnJweZmZnIycmBSqXSjewWFxdb/iLVEoNYIiInZ88/4ESOwN1NwNIR0QBg8DnQ3l46ItoqOeGRkZHIyMhAfHw85s2bh44dOyIhIQEHDx5ESkqK0cd4eHhgx44dOHv2LLp06YI1a9ZgxYoVem1UKhVmz56N9u3bY+jQoWjbti02bdoEAGjatCmSk5ORlJSEkJAQPP/88wCAV199FUuWLMGqVavQvn17DBkyBHv27EFkZKTFz2vJkiXo1q0bli5diuLiYnTr1g3dunXDyZMnLd5XbQli5YQLJ1dUVISAgAAUFhbC399fkn0qlUqkpqZi+PDh8PDwkGSfZB08V/LBcyU9bXUCAHoTvLR/smtTnYDnST7kfq7KysqQnZ2NyMhIeHvX/oqBo5eZU6vVKCoqgr+/vy4v1llVd07NjddYnYCIyAUM7RiGlPExBn/AQx3oDziRtQ3tGIaE6FCbrthF1sMglojIRfAPOJEmtUDKMlpkPwxiiYhcCP+AE5GzcO6ECyIiIiJySgxiiYiIiEh2GMQSERERkewwiCUiIiIi2WEQS0RERESywyCWiIiIiGSHQSwRERGRnQmCgK+++sre3ZAVBrFEREREVpSXl4cXXngBUVFR8PLyQnh4OEaMGIGDBw9a5XiHDx+GIAj466+/rLL/S5cuYcqUKYiMjISPjw9atmyJpUuXoqKiwirHM4WLHRAREZHrUKuAy8eA4uuAXwgQ0Rtwc7fa4S5duoQ+ffqgQYMGWLt2LTp37gylUom9e/di9uzZOHv2rNWOXVeiKEKlUqFePf1w8ezZs1Cr1di8eTNatWqF06dPY9q0aSgpKcG6dets1j+OxBIREZFryNoNvNER+OAR4Mspmt9vdNRst5JZs2ZBEASkp6djzJgxaNOmDTp06IDExEQcP37c6GOMjaRmZmZCEARcunQJAHD58mWMGDECDRs2hK+vLzp06IDU1FRcunQJ8fHxAICGDRtCEARMmjQJgCYoXbt2LaKiouDj44MuXbrgiy++MDju3r17ERsbCy8vL3z//fcG/Rs6dCi2bt2KwYMHIyoqCiNHjsT8+fOxa9cuaV40M3EkloiIiJxf1m5g5wQAov72olzN9ic+BKJHSnrIgoICpKWlYeXKlfD19TW4v0GDBrXe9+zZs1FRUYGjR4/C19cXWVlZ8PPzQ3h4OL788kuMHj0a586dg7+/P3x8fAAAr7zyCnbt2oWUlBS0bt0aR48exfjx49G4cWP0799ft++FCxdi3bp1iIqKMruPhYWFCAwMrPXzqQ0GsURE5DBUahHp2QXIv12G4Pre6BkZCHc3wd7dIrlTq4C0RTAIYIF72wQgLQlo97CkqQXnz5+HKIpo166dZPvUysnJwejRo9GpUycAQFRUlO4+bTAZHBysC0JLSkqwfv16HDp0CHFxcbrH/PDDD9i8ebNeELt8+XIkJCSY3ZcLFy7gzTffxOuvv17Xp2URBrFEROQQ0k7nInlPFnILy3TbwgK8sXRENIZ2DLNjz0j2Lh8Diq5V00AEiq5q2kX2leywoqgJmgVB+i9iL774ImbOnIl9+/Zh0KBBGD16NDp37myyfVZWFsrKygyC04qKCnTr1k1vW2xsrNn9uHbtGoYOHYrHH38cU6dOtexJ1BFzYomIyO7STudi5scZegEsAOQVlmHmxxlIO51rp56RUyi+Lm07M7Vu3RqCIODMmTMWPc7NTROeaYNgAFAqlXptpk6diosXL+KZZ57Br7/+itjYWLz55psm96lWqwEA3377LTIzM3U/WVlZenmxAIymPhhz7do1xMfHIy4uDlu2bDHrMVJiEEtERHalUotI3pNl8kIvACTvyYJKbawFkRn8QqRtZ6bAwEAMGTIEGzduRElJicH9pkpgNW7cGACQm3v/y1tmZqZBu/DwcMyYMQO7du3CvHnz8M477wAAPD09AQAqlUrXNjo6Gl5eXsjJyUGrVq30fsLDwy1+blevXsWAAQMQExODrVu36gJvW2IQS0REdpWeXWAwAluZCCC3sAzp2QW26xQ5l4jegH8TAKYu6wuAf1NNO4lt2rQJKpUKPXv2xJdffonff/8dZ86cwb/+9S9dbmpV2sBy2bJl+O233/Dtt98a5JvOmTMHe/fuRXZ2NjIyMnDo0CG0b98eABAREQFBEPDNN9/gxo0bKC4uRv369TF//nzMnTsXH3zwAS5cuICff/4ZGzduxAcffGDRc7p27RoGDBiA8PBwrFu3Djdu3EBeXh7y8vJq9yLVEoNYIiKyq/zbpgPY2rQjMuDmDgxdc+9G1UD23u2hq61SLzYyMhIZGRmIj4/HvHnz0LFjRyQkJODgwYNISUkx+hgPDw/s2LEDZ8+eRZcuXbBmzRqsWLFCr41KpcLs2bPRvn17DB06FG3btsWmTZsAAE2bNkVycjKSkpIQEhKC559/HgDw6quvYsmSJVi1ahXat2+PIUOGYM+ePYiMjLToOe3btw/nz5/HoUOH0KxZM4SFhel+bIkTu4iIyK6C63tL2o7IqOiRmjJaaYv0J3n5N9EEsBKX16osLCwMb731Ft566y2TbURRhFqtRlFREQCgT58++OWXXwzaaFWX/woAixcvxuLFi/W2CYKAF198ES+++KLRxwwYMEDvGKZMmjRJV3vWnhjEEhGRXfWMDERYgDfyCsuM5sUKAEIDNOW2iOokeqSmjJYNV+wi62E6ARER2ZW7m4ClI6IBmLzQi6UjolkvlqTh5q4po9VpjOY3A1jZYhBLRER2N7RjGFLGxyA0QD9lIDTAGynjY1gnlogMMJ2AiIgcwtCOYUiIDuWKXURkFgaxRETkMNzdBMS1bGTvbhCRDDCdgIiIiIhkh0EsEREREckOg1giIiIikh0GsUREREQkOwxiiYiIiOxMEAR89dVX9u6GrDCIJSIiIpehUqtwIu8EUi+m4kTeCajUKqsfMy8vDy+88AKioqLg5eWF8PBwjBgxAgcPHrTK8Q4fPgxBEPDXX39ZZf8AMHLkSDRv3hze3t4ICwvDM888g2vXrtX8QAmxxBYRERG5hAOXD2B1+mpcL72u2xaiCEFSzyQMihhklWNeunQJffr0QYMGDbB27Vp07twZSqUSe/fuxezZs3H27FmrHFcKoihCpVKhXj3DcDE+Ph5///vfERYWhqtXr2L+/PkYM2YMjh07ZrP+cSSWiIiInN6ByweQeDhRL4AFgPzSfCQeTsSBywesctxZs2ZBEASkp6djzJgxaNOmDTp06IDExEQcP37c6GOMjaRmZmZCEARcunQJAHD58mWMGDECDRs2hK+vLzp06IDU1FRcunQJ8fHxAICGDRtCEARMmjQJgCYoXbt2LaKiouDj44MuXbrgiy++MDju3r17ERsbCy8vL3z//fdG+zh37lz06tULERER6N27N5KSknD8+HEolcq6v2hm4kgsEREROTWVWoXV6ashQjS4T4QIAQLWpK9BfHg83N3cJTtuQUEB0tLSsHLlSvj6+hrc36BBg1rve/bs2aioqMDRo0fh6+uLrKws+Pn5ITw8HF9++SVGjx6Nc+fOwd/fHz4+PgCAV155Bbt27UJKSgpat26No0ePYvz48WjcuDH69++v2/fChQuxbt06REVFmdXHgoICbN++Hb1794aHh0etn5OlGMQSERGRU8vIzzAYga1MhIi80jxk5GegR2gPyY57/vx5iKKIdu3aSbZPrZycHIwePRqdOnUCAERFRenuCwwMBAAEBwfrgtCSkhKsX78ehw4dQlxcnO4xP/zwAzZv3qwXxC5fvhwJCQk19mHRokV46623UFpail69euGbb76R6umZhekERERE5NRulN6QtJ25RFEz8isIgqT7BYAXX3wRK1asQJ8+fbB06VL88ssv1bbPyspCWVkZEhIS4Ofnp/v58MMPceHCBb22sbGxZvVhwYIF+Pnnn7Fv3z64u7tjwoQJuudsCxyJJSIiIqfWWNFY0nbmat26NQRBwJkzZzBq1CizH+fmphljrBwQVs01nTp1KoYMGYJvv/0W+/btw6pVq/D666/jhRdeMLpPtVoNAPj222/RtGlTvfu8vLz0bhtLfTAmKCgIQUFBaNOmDdq3b4/w8HAcP35cN9JrbRyJJSIicjAqtYgfL9zC15lX8eOFW1CpbTe65YxigmMQogiBAOMjogIEhCpCERMcI+lxAwMDMWTIEGzcuBElJSUG95sqgdW4sSaYzs3N1W3LzMw0aBceHo4ZM2Zg165dmDdvHt555x0AgKenJwBApbpfPiw6OhpeXl7IyclBq1at9H7Cw8Nr+xR1tAF3eXl5nfdlLo7EEhEROZC007lI3pOF3MIy3bawAG8sHRGNoR3D7Ngz+XJ3c0dSzyQkHk6EAEFvgpc2sF3Uc5Gkk7q0Nm3ahN69e6Nnz55Yvnw5OnfujLt372L//v1ISUnBmTNnDB6jDSyXLVuGFStW4Pfff8frr7+u12bOnDkYNmwY2rRpgz///BOHDh1C+/btAQAREREQBAHffPMNhg8fDh8fH9SvXx/z58/H3LlzoVar8eCDD6KoqAjHjh2Dn58fJk6caPZzSk9PR3p6Oh588EE0bNgQFy9exJIlS9CyZUubjcICHIklIiJyGGmnczHz4wy9ABYA8grLMPPjDKSdzjXxSKrJoIhBWD9gPYIVwXrbQxQhWD9gvdXqxEZGRiIjIwPx8fGYN28eOnbsiISEBBw8eBApKSlGH+Ph4YEdO3bg7Nmz6NKlC9asWYMVK1botVGpVJg9ezbat2+PoUOHom3btti0aRMAoGnTpkhOTkZSUhJCQkLw/PPPAwBeffVVLFmyBKtWrUL79u0xZMgQ7NmzB5GRkRY9Jx8fH+zatQsPPfQQ2rZti8mTJ6Njx444cuSIQWqCNQmiLTNw7ayoqAgBAQEoLCyEv7+/JPtUKpVITU3F8OHDbVpWgizHcyUfPFfywPMkLZVaxINrDhkEsFoCgNAAb/ywaCDc3SybKCT3c1VWVobs7GxERkbC29u7TvtSqVXIyM/AjdIbaKxojJjgGKuMwNaGWq1GUVER/P39dXmxzqq6c2puvMZ0AiIiIgeQnl1gMoAFABFAbmEZ0rMLENeyke065mTc3dwlLaNF9uPcYT4REZFM5N82HcDWph2Rs2MQS0RE5ACC65t3mdzcdkTOjkEsERGRA+gZGYiwAG8TRaA0ObFhAd7oGRloy24ROSwGsURERA7A3U3A0hHRAGAQyGpvLx0RbfGkLgJg7hx215nr7hQYxBIRETmIoR3DkDI+BqEB+ikDoQHeSBkfwzqxtXHoEBASAmzZUn27LVs07Q4dsk2/qM5YnYCIiMiBDO0YhoToUKRnFyD/dhmC62tSCDgCWwuHDgHDhwMVFcD06Zptzz1n2G7zZmDGDEAQNO1TU4GBA23bV7IYg1giIiIH4+4msIxWXWkDWKXyfpqAsUBWG8ACmnZKJQNZmWA6ARERETmXygGsWq1/3/Tp91MLKgewWmr1/UCWqQUOjUEsEREROY/qAlit6dOBp582DGC1GMji8OHDEAQBf/31l8k227ZtQ4MGDWzWp6oYxBIREZFzEEVg7FhNDqypAFbrk0+qv1+t1uxn7FhJqhbk5eXhhRdeQFRUFLy8vBAeHo4RI0bg4MGDdd631oABAzBnzhxJ9tW7d2/k5uYiICBAkv1ZA4NYIiIicg6CAKxYIV2pLFHU7E+o26S6S5cuoXv37jh06BDWrl2LX3/9FWlpaYiPj8fs2bOl6auZRFHE3bt3a2zn6emJ0NBQCHV87tYkmyB21apV6NGjB+rXr4/g4GCMGjUK586ds3e3iIiIyJE89xzw9tvS7GvzZuPVDCw0a9YsCIKA9PR0jBkzBm3atEGHDh2QmJiI48ePAwAKCwsxffp0tG7dGg0aNMDAgQNx6tQp3T6WLVuGrl274qOPPkKLFi0QEBCAsWPH4vbt2wCASZMm4ciRI9iwYQMEQYAgCLh06ZIuLWDv3r2IjY2Fl5cXvv/+e5SXl+PFF19EcHAwvL298eCDD+LEiRO64xlLJ9i2bRuaN28OhUKBxx57DLdu3dJ7nqdOnUJ8fDzq168Pf39/dO/eHSdPnqzz62eKbILYI0eOYPbs2Th+/Dj279+Pu3fvYvDgwSgpKbF314iIiMiRTJ9e90BWogC2oKAAaWlpmD17Nnx9fQ3ub9CgAURRxMMPP4y8vDzs3LkTJ06cQExMDB566CEUFBTo2l64cAFfffUVvvnmG3zzzTc4cuQIVq9eDQDYsGED4uLiMG3aNOTm5iI3Nxfh4eG6xy5cuBCrVq3CmTNn0LlzZyxcuBBffvklPvjgA2RkZKBVq1YYMmSI3vEq+3//7/9h8uTJmDVrFjIzMxEfH48VK1botXn66afRrFkznDhxAj/99BOSkpLg4eFR59fQFNmU2EpLS9O7vXXrVgQHB+Onn35Cv3797NQrIiIickjaclqmJm9VR6IAFgDOnz8PURTRrl07k22+++47/Prrr8jLy0N5eTn8/f2xbt06fPXVV/jiiy/w3L2+qNVqbNu2DfXr1wcAPPPMMzh48CBWrlyJgIAAeHp6QqFQIDQ01OAYy5cvR0JCAgCgpKQEKSkp2LZtG4YNGwYAeOedd7B//3689957WLBggcHjN2zYgCFDhiApKQkA0KZNGxw7dkwvPsvJycGCBQt0z7V169a1ecnMJpsgtqrCwkIAQGCg6TWky8vLUV5errtdVFQEAFAqlVAqlZL0Q7sfqfZH1sNzJR88V/LA8yQfcj9XSqUSoihCrVZDXdOErcqmTYNw5AiEHTvMfog4bhzEqVNrnhhmJpVKpdnvvf4bc/LkSRQXF6Nx48Z62+/cuYPz589DrVZDFEW0aNECvr6+uv2EhoYiPz9fb79Vj6P9/5iYGN3///7771AqlYiLi9Ntc3d3R48ePZCVlaX3Omv//8yZMxg1apTevnv16oW0tDTdtrlz52Lq1Kn46KOP8NBDD2HMmDFo2bKl0eesfU5KpRLu7u5695n7PpVlECuKIhITE/Hggw+iY8eOJtutWrUKycnJBtv37dsHhUIhaZ/2798v6f7Ieniu5IPnSh54nuRDrueqXr16CA0NRXFxMSoqKsx+nOfWrVDs2AERgDnTk0QAwief4E6PHqiYNKmWvdWnnRyVmZmJgSYWTygtLUVoaCj27NljcF9AQACKiopQXl4ONzc33YAcoBmsu3v3rm7b3bt3UVFRodemtLQUgCZo1G7X5tEWFxfrtVUqlbr9aR93+/ZtuLm54e7duygvL9drX1ZWBlEUddvmzp2LESNGYN++fdi/fz+WLVuG9957D4888ojB86qoqMCdO3dw9OhRg4lm2mPXRJZB7PPPP49ffvkFP/zwQ7XtXn75ZSQmJupuFxUVITw8HIMHD4a/v78kfVEqldi/fz8SEhKsmvdBdcdzJR88V/LA8yQfcj9XZWVluHLlCvz8/ODt7W3egzZvhltiotkBLO61EwEo5s7VHEeClAJ/f38MHjwY77//PhYsWGCQF/vXX38hLi4OK1asQEBAABo1aoT69esbVAXw8vKCu7u7Xvzi7e0NNzc33TYfHx+DNtpBO+1kKwDo2rUrPD09cerUKXTo0AGA5j1y6tQpvPTSS/D39zd4XMeOHfHzzz/r7TszMxOCIOhti4mJQUxMDJKSkjBu3Dh89tlnGDdunMHrUlZWBh8fH/Tr18/gnFYOlKsjuyD2hRdewO7du3H06FE0a9as2rZeXl7w8vIy2O7h4SH5h9ga+yTr4LmSD54reeB5kg+5niuVSgVBEODm5gY3NzPmpG/eDMyaBcD8AFZL295t5kzAzU2SQDYlJQW9e/dGr169sHz5cnTu3Bl3797F/v37kZKSgqysLMTFxWH06NFYvHgxunXrhry8PKSmpmLUqFGIjY3VBbWVn3/VbZGRkUhPT0dOTg78/PwQGBiou6/ya1e/fn3MnDkTixYtQlBQEJo3b461a9eitLQUU6dO1Wur/f+XXnoJvXv3xrp16zBq1Cjs27cPe/fu1bW5c+cOFixYgDFjxiAyMhJ//PEHTp48idGjRxs9Z25ubhAEweh70tz3qGyqE4iiiOeffx67du3CoUOHEBkZae8uERERkaPZsqV2k7mMqbxEbR1ERkYiIyMD8fHxmDdvHjp27IiEhAQcPHgQKSkpEAQBqamp6Nu3L1544QW0a9cOY8eOxaVLlxASEmL2cebPnw93d3dER0ejcePGyMnJMdl29erVGD16NJ555hnExMTg/Pnz2Lt3Lxo2bGi0fa9evfDuu+/izTffRNeuXbFv3z688soruvvd3d1x69YtTJgwAW3atMETTzyBYcOGGU3rlIogilJVBLauWbNm4ZNPPsHXX3+Ntm3b6rYHBATAx8fHrH0UFRUhICAAhYWFkqYTpKamYvjw4bL8dutKeK7kg+dKHnie5EPu56qsrAzZ2dmIjIysPp1AFIGQEODmTWkWPBAEICgIuH69zgsemEObt+rv72/eiLOMVXdOzY3XZPMKpaSkoLCwEAMGDEBYWJju57PPPrN314iIiMgRCALw6aeAp6cmFaA6Tz9d/f1ubpr9fPqpTQJYspxscmJlMmBMREQkSyq1iPTsAuTfLkNwfW/0jAyEu5sMg7eBA4HUVGD4cECpNF4qS1sHtm9f46kHbm6Ah4dmPyYqCpD9ySaIJSIiIutIO52L5D1ZyC0s020LC/DG0hHRGNoxzI49q6XqAtnKCxkYWxCBAaxsyCadgIiIiKSXdjoXMz/O0AtgASCvsAwzP85A2ulcO/WsjrSBrIfH/XQAYytxVV6iVhAYwMoIg1giIiIXpVKLSN6TBWMJe9ptyXuyoFI7RkqfxamF2kA2KKj6pWSnT9fcHxTEANZGpEgTZToBEVEdOU0uIbmc9OwCgxHYykQAuYVlSM8uQFzLRrbrWBXaigqlpaVmVyTSGTjQvOoCzz0HTJvGSVw2ol2Vqy7VMhjEEhHVgdPlEpJLyb9tOoCtTTtrcXd3R4MGDZCfnw9AswpV1RWtnIFarUZFRQXKysqctsSWKIooLS1Ffn4+GjRoAHd391rvi0EsEVEtaXMJq14U0+YSpoyPYSBLDi24vnlLuJrbzppCQ0MBQBfIOiNRFHHnzh34+Pg4ZZBeWYMGDXTntLYYxBIR1UJNuYQCNLmECdGhTC0gh9UzMhBhAd7IKywz+l4WAIQGaFJk7E0QBISFhSE4OBhKpdLe3bEKpVKJo0ePol+/frJclMJcHh4edRqB1WIQS0RUC3LJJSSqjrubgKUjojHz4wwIgF4gq/3qtXREtEN9EXN3d5ckAHJE7u7uuHv3Lry9vZ06iJWKcyZcEBFZmVxyCYlqMrRjGFLGxyA0QD9lIDTAmykx5NA4EktEVAtyyiUkqsnQjmFIiA5llQ2SFQaxRES1IKdcQiJzuLsJTH0hWWE6ARFRLWhzCYH7uYNajppLSETkTBjEEhHVEnMJiYjsh+kERER1wFxCIiL7YBBLRFRHzCUkIrI9phMQERERkewwiCUiIiIi2WEQS0RERESywyCWiIiIiGSHQSwRERERyQ6DWCIiIiKSHQaxRERERCQ7DGKJiIiISHYYxBIRERGR7DCIJSIiIiLZYRBLRERERLLDIJaIiIiIZIdBLBERERHJDoNYIiIiIpKdevbuABEROS+VWkR6dgHyb5chuL43ekYGwt1NsHe3iMgJMIglIllgMCQ/aadzkbwnC7mFZbptYQHeWDoiGkM7htmxZ0TkDBjEEpHDYzAkP2mnczHz4wyIVbbnFZZh5scZSBkfI6tzxy9RRI6HQSwROTRnC4ZcgUotInlPlsE5AwARgAAgeU8WEqJDZREI8ksUkWPixC4iclg1BUOAJhhSqY21IHtJzy7QC/iqEgHkFpYhPbvAdp2qJe2XqKrPR/slKu10rl36pVKL+PHCLXydeRU/XrjFzwC5JI7EEpHD+unyn2YHQ3EtG9muY1St/Numz1lt2tmLo44oc2SYSIMjsUTksG4Wl5vVztGDIVcTXN9b0nb24ogjyo46MkxkDwxiichhBfl5mdXO0YMhV9MzMhBhAd4wNTYpQDNy2DMy0JbdspijjSgzvYZIH4NYInJY3SMaOkUw5Grc3QQsHRENAAbnTnt76Yhoh5/U5Wgjyo44MkxkTwxiichhOUsw5IqGdgxDyvgYhAboB3ihAd6yqSjhaCPKjjYyTGRvnNhFRA5NGwxVncgSyoksDm9oxzAkRIcara8qh7qr2i9RMz/OgADoXca3x5coRxsZJrI3BrFE5PCqC4bIsbm7CQaVI+Q0u96RvkRpR4bzCsuM5sUK9/rF9BpyFQxiiUgWjAVDJD9yXLzCUb5EOdrIMJG9MSeWiIhsQs6z67Vfoh7t2hRxLRvZLVB0hlxjIqlwJJaIiGzCktn1HHU3zVFGhonsjUEsERHZBGfXS4fpNURMJyAiIhvh7HoikhKDWCIisglHq7tKRPLGIJaIiGyCi1c4LpVaxI8XbuHrzKv48cIth5xcR1QVc2KJiMhmHKnuKmnIqW4vUWUMYomIyKY4u95xyLFuL5EWg1giIrI5zq63v5rq9grQ1O1NiA7lFwxySMyJJSIickGW1O0lckQciSUiIqemUotMXTCCdXtJ7hjEEhGR0+KkJdNYt5fkjukERETklLSTlqpeMtdOWko7nWunnjkG1u0luWMQS0RETqemSUuAZtKSK9dDZd1ekjsGsURE5HQ4ack82rq9oQH6KQOhAd4sr0UOjzmxRETkdDhpyXys20tyxSCWiIicDictWYZ1e0mOmE5AREROh5OWiJwfg1giInI6nLRE5PwYxBIRkVPipCUi58acWCIiclqctETkvBjEEhGRU+OkJSLnxHQCIiIiIpIdBrFEREREJDsMYomIiIhIdhjEEhEREZHsMIglIiIiItlhEEtEREREssMgloiIiIhkh0EsEREREckOg1giIiIikh0GsUREREQkO1x2lojIQajUItKzC5B/uwzB9b3RMzIQ7m6CvbtFROSQGMQSETmAtNO5SN6ThdzCMt22sABvLB0RjaEdw+zYMxcjioBgxhcHc9sRkdUwnYCIyM7STudi5scZegEsAOQVlmHmxxlIO51rp565mEOHgJAQYMuW6ttt2aJpd+iQbfpFREYxiCUisiOVWkTyniyIRu7TbkvekwWV2lgLksyhQ8Dw4cDNm8D06aYD2c2bNfffvKlpz0CWyG5kFcQePXoUI0aMQJMmTSAIAr766it7d4mIqE7SswsMRmArEwHkFpYhPbvAdp1yNdoAVqnUpAkAxgPZzZuBGTM0/y+KmvYMZInsRlZBbElJCbp06YK33nrL3l0hIpJE/m3TAWxt2tmbSi3ixwu38HXmVfx44ZbjjyBXDmDVav37KgeylQNYLbWagSyRHclqYtewYcMwbNgwe3eDiEgywfW9JW1nT7KbnFZdAKs1fTpw5AjwySfG768cyKamAgMHWq+/RKRHVkGspcrLy1FeXq67XVRUBABQKpVQKpWSHEO7H6n2R9bDcyUfrnSuujWrj4iGXrheVGY0L1YAEOLvjW7N6jvc61H5PB04cx1zP8uECMDL/X6bP4vvYM6On/DPJ7tiUPsQ+3TUGFEEJk0C3N0BN9MXJUUAwr//DdHHB9XWIhAEzf7On3fIqgWu9JmSM54nDXOfvyCKooNf6zFOEAT8+9//xqhRo0y2WbZsGZKTkw22f/LJJ1AoFFbsHRERERHVRmlpKcaNG4fCwkL4+/ubbOfUQayxkdjw8HDcvHmz2hfFEkqlEvv370dCQgI8PDwk2SdZB8+VfLjiuTpw5jpW/+cs8oruX4oP9fdG0rB2jjWCWYn2PAW2icXUj3+usf37E3ugZ2SgRcew+uuydSswZ07d97Nhg2Yk1kG54mdKjnieNIqKihAUFFRjEOvU6QReXl7w8vIy2O7h4SH5m8Ma+yTr4LmSD1c6V8M6N8Pgjk1luWJXwR0VylU19/Nm6V2Lzmfa6VzM+uTUvTSL+/vP+bMcsz45hZTxMXXPtX3uOU1qwYwZmtSB2uxj82Zg2rS69cNGXOkzJWeufp7Mfe5OHcQSEcmJu5uAuJaN7N0NiwX5GQ4WGGPJ5LSa6ucK0NTPTYgOrXugP306AECoTSC7ebMmECaLcIllkoKsgtji4mKcP39edzs7OxuZmZkIDAxE8+bN7dgzIiLX1T2iIcICvJFXaHpyWmiAt0WpBJbUz5Uk8J8+HVe+TkP4f74y/zFPP80AthZkV8WCHJas6sSePHkS3bp1Q7du3QAAiYmJ6NatG5YsWWLnnhERuS53NwFLR0QDMBzF1N5eOiLaopE2m9fP3bwZ4f/5ymgQbtL27TUvUUt6uMQySUlWQeyAAQMgiqLBz7Zt2+zdNSIilza0YxhSxscgNEA/ZSA0wLtWuas2rZ97byGDWuXEVrdELenhEsskNVmlExARkeMa2jEMCdGhkuQ69owMlDxFwahKK3HVOiPzXk4tUwuqZ/MUEXJ6DGKJiEgyUk1O06YozPw4AwKgF8jWNkXBwJYthkvJ1hYD2Ro52xLLZH+ySicgIiLXIXWKgh5RBF55pcbVtcQqv00SBM3+5Fl63SacaYllcgwciSUicjAsP3SflCkKegQB+PRTYPhwQKkE1GrjzQDg6achbN9uel9uboCHh2Z/DrjkrKOwWYoIuQwGsUREDoTlhwxZrX7uwIFAamr1gay2DmzfvsZTD7QBbGqqZn9kkk1SRMilMJ2AiMhBsPyQHWgDWQ8PTUBaWeWFDKZPB95+W/9+BrAWs2qKCLkcjsRaiUqtQkZ+Bm6U3kBjRWPEBMfA3c3d3t0iIgdl0xWqCECltI3Atmj17g5ET30KQkWFJq/V2Epc2slbM2Zo0gYYwNaK1VJEyOUwiLWCA5cPYHX6alwvva7bFqIIQVLPJAyKGGTHnhGRo2L5IdsyTNvwxCPjV+D1XavhtfofpqsMTJ9+fxLXp58ygK0luS6xTI6F6QQSO3D5ABIPJ+oFsACQX5qPxMOJOHD5gJ16RkSOjOWHbMdU2sa3Qe3Rbto2pPUeUf0OnnsOuH6dASyRnTGIlZBKrcLq9NUQjVwQ1G5bk74GKrXK1l0jkgWVWoUTeSew79I+3W1XwfJDtlHjqlGCYN6qUaxCQGR3DGIllJGfYTACW5kIEXmlecjIz7Bhr4jk4cDlAxjy5RBM3jsZS48tBQA8tvsxl7l6oS0/ZCo0EqCpUsDyQ3VjSdoGETk2BrESulF6Q9J2RK7CVBrOzdKbLpOGoy0/BBguf8ryQ9I5kJVnVjumbZAtqdQifrxwC6m/5upuU80YxEqosaKxpO2IXAHTcO5j+SHrSjudi/f+e8mstkzbcEzaYO/rzKv48cItpwj20k7n4sE1h/DUO8ex8MtfAABD3jjKknpmYHUCCcUExyBEEYL80nyjf5AFCAhRhCAmOMYOvSNyTJak4fQI7WHDntkHyw9ZhzYXtiZcNcpxOeNCINpJhlUjhutFmtrQ/PJaPY7ESsjdzR1JPZMAaALWyrS3F/VcxHqxRJUwDceQtvzQo12bIq5lIwawEqgpF1ZLBNM2HJEzLgRS4yRDwLxJhi6MQazEBkUMwvoB6xGsCNbbHqIIwfoB61knlqgKpuGQLZib4zq5TwuOfDkYZw32OMmw7phOYAWDIgYhPjyeK3YRmYFpOPanW7nKidMXzM1xTYgOtXJPyFLOuhAIa0PXHYNYK3F3c3eJ/D2iutKm4SQeToQAQS+QZRqO9TljnqEx2hJmeYVlRkf0mAvruJw12GNt6LpjOgER2Z2pNJzGisZMw7EiZ8wzNIUlzOTLWYM91oauOwaxROQQBkUMwt7Re/H+kPeR3DsZALBr5C4GsFbirHmG1WEJM3ly1mCPX6zqjukEROQwtGk4ykZKpGalMoXAipw1z7AmLGEmP9pgb+bHGRAAvS9ecg/2tF+sqqb0hPh74+WHO/CLVQ0sCmLv3LmDn376CYGBgYiOjta7r6ysDDt37sSECRMk7SAR2ZdKreIkRSfkrHmG5tCWMCP5MBXshTpB/rbeF6vCEuDKz9g7px+8vTzt3TWHZ3YQ+9tvv2Hw4MHIycmBIAjo27cvduzYgbAwzRunsLAQzz77LINYIidy4PIBrE5frbcYQYgiBEk9k3iZX+acNc+QnJczj6Jrv1gplf5IvfKzUzwnWzA7J3bRokXo1KkT8vPzce7cOfj7+6NPnz7IycmxZv+IyE4OXD6AxMOJBqtp5ZfmI/FwIg5cPmCnnpEUnDXPkJwbFwKhyswOYo8dO4Z//OMfCAoKQqtWrbB7924MGzYMffv2xcWLF63ZRyKyMZVahdXpq43WbdVuW5O+Biq1ytZdI4lwUgkRyZ3ZQeydO3dQr55+9sHGjRsxcuRI9O/fH7/99pvknSMi+8jIzzAYga1MhIi80jxk5GfYsFf2oVKrcCLvBFIvpuJE3gmnCtw5W5+I5MzsnNh27drh5MmTaN++vd72N998E6IoYuTIkZJ3jojs40bpDUnbyZUr5AQ7c54hETk3s0diH3vsMezYscPofW+99RaeeuopiKLz1BMkcmWNFY0lbSdHrpQTzDxDIpIjs4PYl19+GampqSbv37RpE9RqtSSdIiL7igmOQYgiRLfsa1UCBIQqQhETHGPjntkGc4KJiBwfV+wiIgPubu5I6pkEAAaBrPb2op6LnLZeLHOCiYgcH4NYIjJqUMQgrB+wHsGKYL3tIYoQrB+w3mlyQo1hTjARkePjsrNEZNKgiEGID493uRW77JETzJXRiIgswyCWiKrl7uaOHqE97N0Nm9LmBOeX5hvNixUgIEQRIllOsCtUQSAikhrTCYiIqrBlTrArVUEgIpJSrYLYjz76CH369EGTJk1w+fJlAMAbb7yBr7/+WtLOERHZi9Q5wcYWTWAVBCKi2rM4nSAlJQVLlizBnDlzsHLlSqhUmn9cGzRogDfeeAOPPvqo5J0kIrIHqXKCTaULjGkzxuwqCK6W0kFEVBOLg9g333wT77zzDkaNGoXVq1frtsfGxmL+/PmSdo6I5M0ZJivVNSdYmy5QdbQ1vzQfGzM3mrUPVkEgIjJkcRCbnZ2Nbt26GWz38vJCSUmJJJ0iIvkzNvrY0KshXun1Cga3GGzHntmOOekC5nDmldGIyPGo1KIslqK2OIiNjIxEZmYmIiIi9Lb/5z//QXR0tGQdIyL5MjX6+Gf5n5h3ZB6evfksEmMT7dQ726lp0YSaSF0FgYioJmmnc5G8Jwu5hWW6bWEB3lg6IhpDO4bZsWeGLA5iFyxYgNmzZ6OsrAyiKCI9PR07duzAqlWr8O6771qjj0QkI9WNPmpt/d9WdAzq6PQjspakAQgQ9F4zV1gZjYgcS9rpXMz8OMPgX++8wjLM/DgDKeNjHCqQtTiIffbZZ3H37l0sXLgQpaWlGDduHJo2bYoNGzZg7Nix1ugjEcmIuaOPK46vwEPNH3LqAM3cNIDZXWbji9+/MJj4tajnItaJJSKbUKlFJO/JMjr8IAIQACTvyUJCdKjDpBZYFMTevXsX27dvx4gRIzBt2jTcvHkTarUawcHBNT+YiFyCuaOPf5b/6fSz7s1dNGFa52mY1nma7CfBEZF8pWcX6KUQVCUCyC0sQ3p2AeJaNrJdx6phUZ3YevXqYebMmSgvLwcABAUFMYAlIj2WTEJy9ln3liyaoK2CMDxqOHqE9mAAS0Q2lX/bdABbm3a2YPFiBw888AB+/vlna/SFiJxATHAMGno1NKutK8y6l3rRBFtTqUX8eOEWvs68ih8v3IJKbX5VBXJNfM/IU3B9b0nb2YLFObGzZs3CvHnz8Mcff6B79+7w9fXVu79z586SdY6I5MfdzR2v9HoF847Mq7ZdqCLUZWbdS7Vogq3JaZYyOQa+Z+SrZ2QgwgK8kVdYZjQvVgAQGqApt+UoLA5in3zySQDAiy++qNsmCAJEUYQgCLoVvIjIdQ1uMRjP3nwWW/+31ej9AgSXm3Vf10UTbM2cWcoPtQ2yS9+MkUtdS2cmt5ntpM/dTcDSEdGY+XEGBEDvPGo/SUtHRDvU56pWix0QkWszZyWuxNhEdAzqiBXHV+DP8j9120MVoZx17+DMnaU8oHVfG/fMOI7+2Z8cZ7aToaEdw5AyPsbg8xTqoJ8ni4PYqoscEJFrMbYSV4giBEk9kwwC08EtBuOh5g/J7jK6qzN3lvJPl/802cZWOPrnGOQ4s52MG9oxDAnRobK4smFxEPvhhx9We/+ECRNq3RkicmymVuLKL81H4uFEoxOV5HYZ3RVVvRSfV2Te7OObxeVW7ln1ajP6x7QD65DjzHYyzd1NkMWXDYuD2JdeeknvtlKpRGlpKTw9PaFQKBjEEjmp6lbiEiFCgIA16WsQHx7PkVYZMXYpPtDXw6zHBvl54Wal27YOEC0d/WPagfXIcWY7yZ/FQeyffxpePvr9998xc+ZMLFiwQJJOEZHjqWklLhEi8krznH4BA2di6lJ8QYmy2sdpZyl3j2iIvWfu78vWAaIlo39MO5Be5S8tQb5eCPX3xvUi+cxsJ/mzOIg1pnXr1li9ejXGjx+Ps2fPSrFLInIw5i5MYG47cyaHkfVUdym+MnNmKR84cx2zPjll8wDR3FG9ID8vzP/csH8AJx3VlrEvLQ0UHrrXUw4z20n+JAliAcDd3R3Xrl2TandE5GDMXZjAnHaWTA4j66jpUryWn3c93C67q7tdeZayUqkZsV39n7N2CRDNrWsJEZx0JCFTo9qFpZr3Q4DCA3+V3h/Nd9SZ7SR/Fgexu3fv1rstiiJyc3Px1ltvoU+fPpJ1jIgcS0xwDEIUIcgvzTeaFytAQIgipMYFDMyZHNa/SX9J+06GzL0Uf7vsLgJ9PTGqaxMkRIcazXPVTAQzHqBaM0A0t67lzRLzJqDZctKRXCeYmTOZzrueG7ZPfQA3i8tl9dxIfiwOYkeNGqV3WxAENG7cGAMHDsTrr78uVb+IyMG4u7kjqWcSEg8nQoCgF4QK90KGmhYwMHdy2IMjH5T+CZAeSybY/FlSga3/vVSnYMRaAaI5dS1/vHDLrH3ZatKRnCeYmTOZLq+oHG6CgEe7NrVdx8glWRzEqtVqa/SDiGRgUMQgrB+w3mgqgDkLGJg7OezUjVOS9ZmMq+lSfGVSpAVYM0Csqa6lIy2nKfcJZiylRY7EzdIHLF++HKWlpQbb79y5g+XLl0vSKSJyXIMiBmHv6L14f8j7WNN3Dd4f8j7SRqeZlctq7qSvm3du1tyI6kR7KR4wlQigr3JaQFWh/t4m9yFAM8po7QBRW9fy0a5NEdeykV6gXd1z1d5e/HA00rML8HXmVfx44RZU6ppCe8vVdCke0HxRsMaxpcJSWuRILA5ik5OTUVxcbLC9tLQUycnJknSKiBybdgGD4VHD0SO0h9lVBcydHBbkE1SX7pGZtJfiQwPMDziMjbAlDWsHwHSA6Aiz0k0919AAbzzXLxKvfpuFp945jpc+zcRT7xzHg2sOIe10rqR9sKSuraPSjmrb+0sLEVCLIFYURQiC4dv31KlTCAzkm5aITNNODhNM/AkUICBUEYoujbvYuGeua2jHMPywaCAWP9zerPbGRtgGtQ8xGSA60uVx7XPdMa0XNoztih3TemHxw+2x5Wi2QXCpvbwvZSDrDJfizRnVdoQvLeQazM6JbdiwIQRBgCAIaNOmjV4gq1KpUFxcjBkzZlilk0TkHKSYHGZrrlDP1t1NwKQ+kXj3h+xa543KZb31ystpqtQiHlxzyGblwZzlUrw5k+mIbMHsIPaNN96AKIqYPHkykpOTERAQoLvP09MTLVq0QFxcnFU6SUTOw5zJYdr6o/bmSvVszS1XVV0wJ5f11rUsXba2rhxpglldyeVLCzk3s4PYiRMnAgAiIyPRu3dveHiYt7Y2EVFVgyIGIT483qFHOM2pZ+tsgayrjbDZ+vK+FF8UHIncvrSQ87G4xFb//veLkN+5c8dgxMTf37/uvSIiSTjypXDt5DBHZG492/jweId5PaXiSiNs9ri872pfFIisyeIgtrS0FAsXLsTOnTtx65ZhAWmVSiVJx4jkylECR1e6FC41c+vZZuRnOGwgXheuMsJmr8v7rvRFgciaLA5iFyxYgO+++w6bNm3ChAkTsHHjRly9ehWbN2/G6tWrrdFHItlwlMDRFS+FS8ncerbmtiPHZM/L+7b8oqBd4hbQ5AH3ahXMgJnM5shLJFscxO7ZswcffvghBgwYgMmTJ6Nv375o1aoVIiIisH37djz99NPW6CeRw3OUwNGVL4VLxdx6tua2I8fl7Jf3tUvcFhTfwdqewOQPTiDQz8cpnhtZn6MvkWxxEFtQUIDIyEgAmvzXggLNt7sHH3wQM2fOlLZ3RDLhSIGjq1wKt2bahraebX5pvtFzKkBAiCIEMcExkhyP7MtZL+9XXuLWq9JHQy5L3JJ9yWGJZIsXO4iKisKlS5cAANHR0di5cycAzQhtgwYNpOwbkWxYEjhamytcCj9w+QCGfDkEk/dOxqLvF2Hy3skY8uUQHLh8QJL9a+vZAjBYmMFR69lS3VS3bK0cOcMSt2Q/cnn/WBzEPvvsszh16hQA4OWXX8amTZvg5eWFuXPnYsGCBZJ3kEgOHClwdPZL4dq0japfGrRpG1IFstp6tsGKYL3tIYoQ5hSTw3OGJW7JfuTy/rE4nWDu3Lm6/4+Pj8fZs2dx8uRJtGzZEl26cKlIck2OFDg686VwW6dtyKGeLZExzrDELdmPXN4/FgexlZWVlaF58+Zo3ry5VP0hkiVHChzluLSrueyR7ytFPVtHKbtGrsNZlrgl+5DL+8fidAKVSoVXX30VTZs2hZ+fHy5evAgAWLx4Md577z3JO0gkB46WQ+msl8IdKW3DXNbO3yUyRlsD11RmrwDNLHM5LHFLtieX94/FQezKlSuxbds2rF27Fp6enrrtnTp1wrvvvitp54jkxNECx0ERg7B39F68P+R9rOm7Bu8PeR9po9NkG8ACjpW2YQ5b5e8SVaWtgQvAIBCR4xK3ZFtyef9YnE7w4YcfYsuWLXjooYcwY8YM3fbOnTvj7NmzknaOSG5qyqG09WVlR17atTYcKW2jJo5Udo1cU+UauAXFd3TbnaUGLlmXHGooWxzEXr16Fa1atTLYrlaroVQqJekUkZyZChwdZTUvOZNTvq+r1Oslx6atgXv8fD5unjmO9yf24IpdZDZHr6FscTpBhw4d8P333xts//zzz9GtWzdJOkXkbHhZWTqOlrZhihzzd8k5ubsJutxFRwpASB4cuYayxSOxS5cuxTPPPIOrV69CrVZj165dOHfuHD788EN888031ugjkazxsrL05FD6Sm75u0REcmPxSOyIESPw2WefITU1FYIgYMmSJThz5gz27NmDhIQEa/RRz6ZNmxAZGQlvb290797d6KgwkSNxpNW8nIk2bWN41HD0CO3hUAEscD9/t2q1Ci0BAkIVoQ6Rv0tEJEdmB7EXL16EKGpGkoYMGYIjR46guLgYpaWl+OGHHzB48GCrdVLrs88+w5w5c/B///d/+Pnnn9G3b18MGzYMOTk5Vj82UW3xsrJrcrSya0REzsbsILZ169a4ceP+H9knn3wS16+bHl2yhvXr12PKlCmYOnUq2rdvjzfeeAPh4eFISUmxaT+ILMHLyq5LLvm7RERyZHZOrHYUVis1NRWrVq2SvEOmVFRU4KeffkJSUpLe9sGDB+PYsWNGH1NeXo7y8nLd7aKiIgCAUqmUrJKCdj+szOD47HWuOjXshGaKZrhZetNkWajGisbo1LAT30f3ONPnqn+T/nhw5IM4deMUbt65iSCfIHRp3AXubu6yf37OdJ6cHc+VPPA8aZj7/Ou07Kwt3bx5EyqVCiEhIXrbQ0JCkJeXZ/Qxq1atQnJyssH2ffv2QaFQSNq//fv3S7o/sh57nKsZnjMAz+rb7E3ba5vOyIgzfq7y7v3nTJzxPDkrnit5cPXzVFpaalY7s4NYQRAgCILBNlurekxRFE324+WXX0ZiYqLudlFREcLDwzF48GD4+/tL0h+lUon9+/cjISEBHh4ekuyTaqZSq4yObFXH3ufq8JXDWP/Ter3c12BFMOZ2n4sB4QNs3h9HZu9zRebheZIPnit54HnS0F45r4lF6QSTJk2Cl5cXAKCsrAwzZsyAr6+vXrtdu3ZZ0E3zBQUFwd3d3WDUNT8/32B0VsvLy0vX38o8PDwkf3NYY59kXF0XDbDXuUqISsDAFgMduiyUo+HnSh54nuSD50oeXP08mfvczQ5iJ06cqHd7/PjxlvWojjw9PdG9e3fs378fjz32mG77/v378eijj9q0L2Q/2kUDquaWahcNcPTJMs62DCwREZG9mB3Ebt261Zr9MEtiYiKeeeYZxMbGIi4uDlu2bEFOTg5mzJhh766RDXDRABkSRcCctCNz2xEREd1j8WIH9vTkk0/ijTfewPLly9G1a1ccPXoUqampiIiIsHfXyAa4aIDMHDoEhIQAW7ZU327LFk27Q4ds0y8itQrI/h749QvNb7XKOY5F5GJkU51Aa9asWZg1a5a9u0F2wEUDZOTQIWD4cKCiApg+XbPtuecM223eDMyYoRmFHT4cSE0FBg60bV/JtWTtBtIWAUXX7m/zbwIMXQNEj5TvsYhckKxGYsm1cdEAmdAGsEqlJk0A0ASyVUdktQEsoGmnVGoexxFZspas3cDOCfpBJQAU5Wq2Z+2W57GIXBSDWJINrkUvA5UDWLVa/77KgWzlAFZLrb4fyB49apv+kutQqzSjokZy6nXb0pKkudxvy2MRuTAGsSQbXIvewVUXwGpNnw48/bRhAKulDWTHjLFeP8k1XT5mOCqqRwSKrmrayelYRC6MQSzJCteid1CiCIwdq8mBNRXAan3ySfX3q9Wa/Wj3SySFYtOTQmvVzlGOReTCZDexi2hQxCDEh8dz0QBHIgjAihX3J3HVlTZ4Zdktkoqf8UVxat1Oin34Mn+fqC4YxJIscdEAB/Tcc5rgU4q6zRs21H0fRJVF9NZUBijKhfFcVUFzf0RvGxzrnn9PB4atZaUColpiOgERSWf6dODtt+u2j82bgUmTJOkOkY6bu6a0FQAYTA69d3voak07qx6rktt5rFRAVAcMYolIWnUJZDdvNl5PlkgK0SOBJz4E/MP0t/s30WyXckTU1LH0sFIBUV0wnYCIpDd9uqZMVk2TuCp7+mmXDWBVahVzvG0leiTQ7mFNZYDi65r81Yje0ozAGjuWlz/w0aPVNKpUqSCyr/R9IHJiDGKJSHqbN1sWwALA9u1Av34uF8geuHwAq9NX6y2pHKIIQVLPJFbbsBY3d9sFjKU3zWvHSgVEFmM6ARFJy9hCBuYytrKXEztw+QASDyfqBbAAkF+aj8TDiThw+YCdekaSsWVVBCIXwyCWiKRTlwBWa/p0YNs2SbrjyFRqFVanr4ZoZPa6dtua9DVQMVdS3rSVCkxO8BIA/6bSVEUgcjEMYolIGlu2SFNeCwBeekma/TiwjPwMgxHYykSIyCvNQ0Z+hvU6oVYB2d8Dv36h+W2rgNlex7UHW1ZFIPtzpfe2A2BOLBHVnSgCr7yiWZxAilW2tIscOPGKXTdKb0jazmJZu4G0RfrLo/o30QRc1qxbaq/j2pO2UoHR573aeZ+3q3HF97adcSSWiOpOEIBPPwU8PQG3Gv5Zefrp6u93c9PsR7tfJ9VYYd5qTea2s0jWbk190sp/bAFNcX5r1i2113EdQfRIYM5pYOI3wOj3NL/n/Mrgxlm48nvbjhjEEpE0Bg4EUlMBDw/TgezmzcDHH5uuI+vmpnn8F19Yr58OIiY4BiGKEAgmciUFCAhVhCImOEbaA6tVmtEioytJWbFuqb2O60i0VRE6jdH8ZgqBc+B7224YxBKRdKoLZCsvZGBsQQRtAJuaqim15eTc3dyR1DMJAAwCWe3tRT0XSV8v9vIxw9EiPZXqljrDcYmsje9tu2EQS0TSqhzIatMBjK3EVTmQFYT7AezAgbbtr0RUahVO5J1A6sVUnMg7YVZVgUERg7B+wHoEK4L1tocoQrB+wHrr1Ik1tx6p1HVL7XVcImvje9tuOLGLiKSnDWTHjgVWrDC9gMH06ZoA9pVXNDm1Mg1g67JgwaCIQYgPj7fdil1S1S1Vq4DLP2r+//KPQFSf6i+P1/a4apVtVtciqi3WArYbBrFEZB0DBwLXr9c8Oeu554Bp0ySbxGXrJVy1CxZUrfeqXbDAnBFVdzd39AjtYbU+6tHWLS3KhfEcPkFzf3V1S7WzsIsLgC5bgE8eB/wCq5+FXZvjcrY3yYEUnymqFaYTEJH1mBuYShTAHrh8AEO+HILJeydj0feLMHnvZAz5cohu5avaXPKvjiwXLKhr3dLazsK29Lic7U1ywVrAdsMgloicQk1LuK4/ub7aALcyc4Ndh1iwoDa0dUv9w/S3+zfRbDc1ylnXWdjmHpezvUluavuZojphOgERyZ45I6Jb/7fV4D5jl/wtyW+1+4IFdRE9Emj3sGX5ppbMwo7sW/vjSnEcIlurzWeK6oRBLBHJXk0joqaIECFAwJr0NYgPj8d3V76zKL/VrgsWSEFbt9RcUs3Crum4nO1NcmXpZ4rqhOkERCR7dRnp1F7yP5F3wuL8VrstWGAvtpqFzdneRGQGBrFEJHtSjHSeuH7C4vxWuy1YYC/aWdgmgnbNLOymdZ+FbavjEJGsMYglItmraUTULMbmEBlRddTXLgsW2IutZmHbe7a3WgVkfw/8+oXmNyeQETkk5sQSkexpR0QTDydCgGA0JcAUAQJCFCHoGdYTW37dUmN7Y6O+Nl+wwJ60s7C1dWK1/JtoAkupZmFXPo5BnVgJj1MVa9MSyQaDWCJyCtoR0aqVBUIVoRgWOQzb/rcNAPQC3MqX/GNDYhGiCEF+ab7RIFgb7JrKb7XpggX2pp2FffG/wP/+BMZ9XvOKXXU5jq1me2tr01Y9/9ratCyVRORQGMQSyYytV6SSk+pGRDs37my0dNainot0l/xNjeY6ZX5rXbm5AxFxwP9SNb+t9brYarZ3jbVpBU1t2nYPs2QSkYNgEEskI5bUMHVVpkZEzbnkb2o0t2qwS06ItWmJZIdBLJFMaFekMreGKRky55K/S+W30n2sTUskOwxiiWSgphWpKhfsZ7BVdy6V30oarE1LJDsssUUkAzWtSGWshinJj0qtwom8E0i9mIoTeSf0FlYgK2NtWiLZ4UgskQyYuyJVXVauIvtivrOdaWvT7pwATSBb+aqHDWrTEpHFOBJLJAPmrkglxcpVZHvafOeqo+3afOcDlw/YqWcuRlub1j9Mf7t/E5bXInJAHIklkgHtilS1rWHqyhy9JBnznc2gVtmuVqyta9MSUa0xiCWSgepWpGINU9PkcIneknxnl5xsZo8VtGxVm5aI6oTpBEQyoa1hGqwI1tseoghheS0j5HKJnvnO1dCuoFW1fqt2Ba2s3fbpF1FN1Cog+3vg1y80vzlJ0yo4EkskI6xhah45XaJnvrMJXEGL5MoeVw9cFEdiiWRGW8N0eNRw9AjtYfcgzBHJqSSZNt9ZMFHaSYCAUEWo6+U7W7KCFpGj4NUDm2IQS0ROR06X6LX5zgAMAlmXznfmClokNzVePYDm6gFTCyTDIJbICBadty2pX2+5XaJnvrMRXEGL5IZXD2yOObFEVchhRrszMfZ6N1M0wwzPGbXepxxLkjHfuQrtClpFuTA+siVo7ucKWuQoePXA5jgSS1SJXGa0OwtTr/fN0psAgMNXDtdqv3K9RM9850q0K2gBMFwKlitokQPi1QObYxBLdE9NM9oBYE36GqYWSMSc1/ufP/2z1q83L9E7Aa6gRXKivXpgYpKm5upBU149kBDTCYjuYdF526rp9QY0I+B1eb15id4JcAUtkgvt1YOdE6AJZCt/QefVA2tgEEt0j5xmtDsDW73e2kv0JGNcQYvkQnv1wGid2NW8eiAxBrFE98htRrvc8fUmIqfEqwc2wyCW6B45zmiXs5pebwAIVgTz9SbHoFYxKCHz8eqBTXBiF9E9cp3RLlfmvN5zu8/l6032l7UbeKMj8MEjwJdTNL/f6MjVl4jsjEEsUSWc0W5bpl5vbQrBgPABdugVUSVcRpTIYTGdgKgKzmi3LWOvd6eGnbA3ba+9u0aursZlRAXNMqLtHmZqAZEdMIglMoIz2m2r6uutVCrt2BuieyxZRpT5j0Q2x3QCIiIiY7iMKJFD40gsERG5NlOVB7iMKJFDYxBL5GBUahXzcYlsJWu3icL0azS5rv5NNJO4jObFCpr7uYwokV0wiCVyIAcuH8Dq9NV6y7GGKEKQ1DOJlRGIpKatPFA1QNVWHnjiQy4jSuTAmBNL5CAOXD6AxMOJegEsAOSX5iPxcCIOXD5gp54ROaEaKw/gfuWBJz4E/MP0m/g30WznMqJEdsORWCIHoFKrsDp9tdGVq0SIECBgTfoaxIfHM7WApOHqK1BZUnmAy4gSOSQGsUQOICM/w2AEtjIRIvJK85CRn8HSX1R31eWBusrIoqWVB7iMKJHDYToBkQO4UXpD0nZEJnEFKg1WHiCSPQaxRA5Au8yqVO2IjDI3D1StsmWv7COit2b0WTtBy4AA+Ddl5QEiB8YglsgBxATHIEQRAsHEH1QBAkIVoYgJjrFxz0g21Cog+3vg1y80v40FopbkgTo7N3dN+gQAw0CWlQeI5IBBLJEDcHdzR1LPJAAwCGS1txf1XMRJXfeo1CqcyDuB1IupOJF3AipXGDmsTtZu4I2OwAePAF9O0fx+o6NhagBXoNIXPdJ45YH6YeZVHhCNjWgTka1wYheRgxgUMQjrB6w3Wid2Uc9FrBN7D2vpVmFOrVNtMMY8UENVKw/88gcw9x9A/TwguprHbdkCvPIK8OmnwMCBNusu3ePq1TUIAINYIocyKGIQ4sPjuWKXCdpaulVLkWlr6a4fsN61Atkac1yF+7VO3dzv54FyBSp92soDhw4BU8YDFRXA9Oma+557zrD95s3AjBmAIADDhwOpqQxkbYnVNegephMQORh3N3f0CO2B4VHD0SO0BwPYe2qqpQsAa9LXuFZqgaU5rswDNe3QIU1AqlTeTxOYPl0z4lqZNoAFNO2USs3jDh2ybX9dFatrUCUMYolIFiyppesyapPjaioP1JVXoKocwKrV+vdVDmQrB7BaarXtAllzJu85M1bXoCqYTkBEssBaukbUNseVK1DdV10AqzV9OnDkCPDJJ8bvrxzIWiu1gJfQLbvywIUpXAKDWCKSBdbSNaIuOa5cgUqTDjB2rCYHtqZKA6YCWC21WrOfsWOB69c1+bJSsWTynjNjdQ2qgukERCQLrKVrBHNc60YQgBUrpCuVJYqa/UkZwPIS+n2srkFVMIglIllgLV0TmONaN889B7z9tjT72rzZeDWDuuACFfdxlTWqgukERCQbrKVrAnNc60ZbTqvqpC1LWCOABax/CV1O9Va1Vx52ToAmkK08Os0rD66IQSwRyQpr6ZrAHNe6qUsga60AFrDuJXQ5ThbTXnkw2u/VjttvsgoGsUQkO9paukSSmj4dOHq05klclT39tPUCWMB6C1TIebIYrzzQPcyJJSIiAjQjqpYEsACwfbvhgghSssbkPWeYLKa98tBpjOY3A1iXxCCWiIjI2EIG5jK2speUpJ68x8li5CRkk06wcuVKfPvtt8jMzISnpyf++usve3eJiIicQV0CWC1tTq21UgukvITOeqvkJGQTxFZUVODxxx9HXFwc3nvvPXt3h4iInMGWLXUPYLWsHchKNXmP9VbJScgmnSA5ORlz585Fp06d7N0VIiJyBqIIvPKKdIsTCIJmf1ItnmAtrLdKTkI2I7G1UV5ejvLyct3toqIiAIBSqYRSqZTkGNr9SLU/sh6eK/nguZIHpzhPO3YAY8YASqVm6VhTnngC2LnT9P1uboCHh2Z/d+9K38860j9XHkDCauDf90aOjdVbTVgFqNSaH7IZp/hMScDc5y+IoqN/ZdS3bds2zJkzx6yc2GXLliE5Odlg+yeffAKFQmGF3hERERFRXZSWlmLcuHEoLCyEv7+/yXZ2HYk1FWRWduLECcTGxtZq/y+//DISExN1t4uKihAeHo7BgwdX+6JYQqlUYv/+/UhISICHh4ck+yTr4LmSD54reXCq83T0qPER2Q0bgEmT7t/euhWYM+f+be0I7BdfAP362aq3FjN5rtQq4Eo6UJIP+AYD4T1ZrsqOnOozVQfaK+c1sWsQ+/zzz2Ps2LHVtmnRokWt9+/l5QUvLy+D7R4eHpK/OayxT7IOniv54LmSB6c4Tw89BHz5JTB8OFBRoclr3bwZmDZNv91zz2numzFDkwPr6Qns3g0MHGifflvI8Fx5AK0cN/h2VU7xmaoDc5+7XYPYoKAgBAUF2bMLREREGgMHAqmpwNixwIoVpqsMTJ9+fxLXp5/KJoAlcjaymdiVk5ODgoIC5OTkQKVSITMzEwDQqlUr+Pn52bdzRERUM7XK8ZcKHTgQuH695ooFzz2nGaWVqrIBEVlMNkHskiVL8MEHH+hud+vWDQDw3XffYcCAAXbqFRERmSVrt2ap08orRfk30SypaumKU9ZmbmDKAJbIrmRTJ3bbtm0QRdHghwEsEZGDy9oN7JxguNRpUa5me9Zu+/SLiGRNNkEsERHJkFqlGYGFsWqO97alJWnaERFZgEEsERFZz+VjhiOwekSg6KqmHRGRBRjEEhGR9RRfl7YdEdE9DGKJiMh6/EKkbUdEdI9sqhMQEZEMRfTWVCEoyoXxvFhBc39Eb81NOZThIiKHwCCWiIisx81dU0Zr5wQAAvQD2Xslqoau1rSTUxkuIrI7phMQEZF1RY8EnvgQ8A/T3+7fRLM9eiTLcBGRxTgSS0RE1qNND1BVAKPeBkQRKL2pnypQYxkuQVOGq93DTC0gIh0GsUREZB3VpQdE9r2/zZIyXJUfR0QujekERCQJlVqFE3knkHoxFSfyTkDF4vWuzZL0AJbhIqJa4EgsEdXZgcsHsDp9Na6X3g8yQhQhSOqZhEERg+zYM7ILS9MDWIaLiGqBI7FEVCcHLh9A4uFEvQAWAPJL85F4OBEHLh+wU8/IbixdpUtbhktbrcCAAPg3vV+Gi6xHrQKyvwd+/ULzm1dUyIFxJJaIak2lVmF1+mqIRkbcRIgQIGBN+hrEh8fDnRNyXIel6QGWlOEi61CrgKPrgP+3Cbjz1/3tLHFGDowjsURUaxn5GQYjsJWJEJFXmoeM/Awb9orsrjbpAeaU4SLryNoNvNYKOPwP/QAWYIkzcmgciSWiWrtRekPSduQkLF2lSyt6pCZPVqoVu7j6V820E/CMnieAJc7IkTGIJaJaa6xoLGk7chJ1SQ9wc5emjBZX/6pZtRPwKmOJM3JMTCcgolqLCY5BiCIEgokJOQIEhCpCERMcY+Oekd3ZMz2Aq3+Zp8YJeFWwxBk5GI7EElGtubu5I6lnEhIPJ0KAoDfBSxvYLuq5iJO6XJXU6QHm4Opf5rM0KGWJM3IwHIklojoZFDEI6wesR7AiWG97iCIE6wesZ51YV6dND+g0RvPb2oGjpeW9XJklQSlLnJED4kgsEdXZoIhBiA+PR0Z+Bm6U3kBjRWPEBMdwBJZsj6t/ma/GCXiVsMQZOSAGsUQEQFPztS5BqLubO3qE9rBiD4nMwNW/zFftBLx7fAKBERs4GY4cEoNYIuKyseQ8alvey1VpJ+BVreTgEwg8MAPoN58jsOSwGMQSuTjtsrFVV93SLhvLvFaSFa7+ZTl7TMAjkgAndhG5sJqWjQWANelroOL66SQnXP3LcraegEckAY7EErkwS5aNZb4ryQpHF4mcHoNYIhfGZWPJqUm1+hcROSSmExC5MC4bS0REcsWRWCIXpl02Nr8032herAABIYoQLhtLRARoVoRjiorDYBBL5MLktmxsXWvZEhHVWtZuw1Jk/k001TA4WdAuGMQSuTjtsrHG6sQu6rnIYcprsZYtEdlN1u57ZduqXLEqytVsZ9ULu2AQS0QOv2wsa9m6GF6yJUeiVmlGYI0uniECEIC0JE01DL5PbYpBLBEBcNxlY2uqZStAwJr0NYgPj3eYoJvqgJdsydFcPqb/fjQgAkVXNe1YDcOmWJ2AiByaJbVsSea0l2yrBgzaS7ZZu+3TL3Jtxab//alVO5IMg1gicmisZVuFWgVkfw/8+oXmt7OsplbjJVtoLtk6y/Ml+fALkbYdSYbpBETk0FjLthJnvtTOS7bkqCJ6az5nRbkw/iVL0Nwf0dvWPXN5HIklIoemrWWrLflVlQABoYpQ569l6+yX2nnJlhyVm7vmiyIAGPw7dO/20NWc1GUHDGKJyKFpa9kCMAhkHbGWrVW4wqV2XrIlRxY9UlNGyz9Mf7t/E5bXsiOmExCRw5NLLVurcYVL7bxkS44ueqSmjBbLvzkMBrFEJAvm1LJ12hW9XOFSu/aS7c4J0FyirRzI8pItOQg3d/l+UXRCDGKJSDaqq2Xr1Ct6ucqldu0lW6OT11bzki0R6WEQS0Sy5/QrernSpXZesiUiM3FiFxHJWk0regHAmvQ1UMl50pOrzY7WXrLtNEbz21meFxFJikEsEcmay6zoxdnRRER6mE5ARLLmUit68VI7EZEOg1gikjWXW9GLs6OJiAAwnYCIZI4rehERuSYGsUQka1zRi4jINTGIJSLZ067oFawI1tseogiRf3ktIiIyijmxROQUzFnRi4iInAeDWCJyGtWt6EVERM6F6QREREREJDsMYomIiIhIdhjEEhEREZHsMIglIiIiItlhEEtEREREssMgloiIiIhkh0EsEREREckOg1giIiIikh0GsUREREQkOwxiiYiIiEh2uOwsERGRKWoVcPkYUHwd8AsBInoDbu727hURgUEsERGRcVm7gbRFQNG1+9v8mwBD1wDRI+3XLyICwHQCIiIiQ1m7gZ0T9ANYACjK1WzP2m2ffhGRDoNYIiKiytQqzQgsRCN33tuWlqRpR0R2wyCWiIiossvHDEdg9YhA0VVNOyKyGwaxRERElRVfl7YdEVkFg1giIqLK/EKkbUdEVsEgloiIqLKI3poqBBBMNBAA/6aadkRkNwxiiYiIKnNz15TRAmAYyN67PXQ168US2RmDWCIioqqiRwJPfAj4h+lv92+i2c46sUR2x8UOiIiIjIkeCbR7mCt2ETkoBrFERESmuLkDkX3t3QsiMoLpBEREREQkOxyJJSIi16BWMTWAyIkwiCUiIueXtVuzlGzllbj8m2iqEHCSFpEsMZ2AiIicW9ZuYOcEw6Vki3I127N226dfRFQnDGKJiMh5qVWaEViIRu68ty0tSdOOiGSFQSwRETkntQr4f28bjsDqEYGiq5pcWSKSFVkEsZcuXcKUKVMQGRkJHx8ftGzZEkuXLkVFRYW9u0ZERI4oazfwRkdg79/Na1983br9ISLJyWJi19mzZ6FWq7F582a0atUKp0+fxrRp01BSUoJ169bZu3tERORItDmwRlMITPALsVp3iMg6ZBHEDh06FEOHDtXdjoqKwrlz55CSksIglojImVlaFqvaHFhjBE2VgojeUvSWiGxIFkGsMYWFhQgMDKy2TXl5OcrLy3W3i4qKAABKpRJKpVKSfmj3I9X+yHp4ruSD50oerH6ezqYCB5YCt3Pvb6sfBgxKBtoNN/6Yyz8CxQWAm7cZBxA0vxJWASq15sdJ8TMlDzxPGuY+f0EURQuutziGCxcuICYmBq+//jqmTp1qst2yZcuQnJxssP2TTz6BQqGwZheJiIiIqBZKS0sxbtw4FBYWwt/f32Q7uwaxpoLMyk6cOIHY2Fjd7WvXrqF///7o378/3n333Wofa2wkNjw8HDdv3qz2RbGEUqnE/v37kZCQAA8PD0n2SdbBcyUfPFfyYLXzpFYBm3rpj8DqETQjsrN+NEwtuPwj8MnjNR/joaVA7GSXWbGLnyl54HnSKCoqQlBQUI1BrF3TCZ5//nmMHTu22jYtWrTQ/f+1a9cQHx+PuLg4bNmypcb9e3l5wcvLy2C7h4eH5G8Oa+yTrIPnSj54ruRB8vOUfRwozK6+TeFF4NoJILKv/vaoPoBfoGYhA6N5sfdyYOOmu0wAWxk/U/Lg6ufJ3Odu1yA2KCgIQUFBZrW9evUq4uPj0b17d2zduhVubrKoDkZERJYyt9yVsXZu7pqlZHdOgCbntXIgey8HduhqlwxgiZyNLCLBa9euYcCAAQgPD8e6detw48YN5OXlIS8vz95dIyIiqZlb7spUu+iRwBMfAv5h+tv9m2i2R4+sW/+IyCHIojrBvn37cP78eZw/fx7NmjXTu0+G89KIiKg6Eb01AWdNKQHVlcWKHgm0e9iy8lxEJCuyGImdNGkSRFE0+kNERE5GmxIAQJcCoGNBSoCbuyZnttMYzW8GsERORRZBLBERuRimBBBRDWSRTkBERC6IKQFEVA0GsURE5Li0KQFERFUwnYCIiIiIZIdBLBERERHJDoNYIiIiIpIdBrFEREREJDsMYomIiIhIdhjEEhEREZHsMIglIiIiItlhEEtEREREssMgloiIiIhkh0EsEREREckOg1giIiIikh0GsUREREQkOwxiiYiIiEh26tm7A7YkiiIAoKioSLJ9KpVKlJaWoqioCB4eHpLtl6THcyUfPFfywPMkHzxX8sDzpKGN07RxmykuFcTevn0bABAeHm7nnhARERFRdW7fvo2AgACT9wtiTWGuE1Gr1bh27Rrq168PQRAk2WdRURHCw8Nx5coV+Pv7S7JPsg6eK/nguZIHnif54LmSB54nDVEUcfv2bTRp0gRubqYzX11qJNbNzQ3NmjWzyr79/f1d+g0nJzxX8sFzJQ88T/LBcyUPPE+odgRWixO7iIiIiEh2GMQSERERkewwiK0jLy8vLF26FF5eXvbuCtWA50o+eK7kgedJPniu5IHnyTIuNbGLiIiIiJwDR2KJiIiISHYYxBIRERGR7DCIJSIiIiLZYRBLRERERLLDIFZiI0eORPPmzeHt7Y2wsDA888wzuHbtmr27RZVcunQJU6ZMQWRkJHx8fNCyZUssXboUFRUV9u4aGbFy5Ur07t0bCoUCDRo0sHd3qJJNmzYhMjIS3t7e6N69O77//nt7d4mqOHr0KEaMGIEmTZpAEAR89dVX9u4SGbFq1Sr06NED9evXR3BwMEaNGoVz587Zu1sOj0GsxOLj47Fz506cO3cOX375JS5cuIAxY8bYu1tUydmzZ6FWq7F582b873//wz//+U+8/fbb+Pvf/27vrpERFRUVePzxxzFz5kx7d4Uq+eyzzzBnzhz83//9H37++Wf07dsXw4YNQ05Ojr27RpWUlJSgS5cueOutt+zdFarGkSNHMHv2bBw/fhz79+/H3bt3MXjwYJSUlNi7aw6NJbasbPfu3Rg1ahTKy8vh4eFh7+6QCa+99hpSUlJw8eJFe3eFTNi2bRvmzJmDv/76y95dIQAPPPAAYmJikJKSotvWvn17jBo1CqtWrbJjz8gUQRDw73//G6NGjbJ3V6gGN27cQHBwMI4cOYJ+/frZuzsOiyOxVlRQUIDt27ejd+/eDGAdXGFhIQIDA+3dDSJZqKiowE8//YTBgwfrbR88eDCOHTtmp14ROY/CwkIA4N+lGjCItYJFixbB19cXjRo1Qk5ODr7++mt7d4mqceHCBbz55puYMWOGvbtCJAs3b96ESqVCSEiI3vaQkBDk5eXZqVdEzkEURSQmJuLBBx9Ex44d7d0dh8Yg1gzLli2DIAjV/pw8eVLXfsGCBfj555+xb98+uLu7Y8KECWDWhvVZep4A4Nq1axg6dCgef/xxTJ061U49dz21OVfkeARB0LstiqLBNiKyzPPPP49ffvkFO3bssHdXHF49e3dADp5//nmMHTu22jYtWrTQ/X9QUBCCgoLQpk0btG/fHuHh4Th+/Dji4uKs3FPXZul5unbtGuLj4xEXF4ctW7ZYuXdUmaXnihxLUFAQ3N3dDUZd8/PzDUZnich8L7zwAnbv3o2jR4+iWbNm9u6Ow2MQawZtUFob2hHY8vJyKbtERlhynq5evYr4+Hh0794dW7duhZsbL0rYUl0+U2R/np6e6N69O/bv34/HHntMt33//v149NFH7dgzInkSRREvvPAC/v3vf+Pw4cOIjIy0d5dkgUGshNLT05Geno4HH3wQDRs2xMWLF7FkyRK0bNmSo7AO5Nq1axgwYACaN2+OdevW4caNG7r7QkND7dgzMiYnJwcFBQXIycmBSqVCZmYmAKBVq1bw8/Ozb+dcWGJiIp555hnExsbqrmbk5OQwt9zBFBcX4/z587rb2dnZyMzMRGBgIJo3b27HnlFls2fPxieffIKvv/4a9evX113lCAgIgI+Pj51758BEkswvv/wixsfHi4GBgaKXl5fYokULccaMGeIff/xh765RJVu3bhUBGP0hxzNx4kSj5+q7776zd9dc3saNG8WIiAjR09NTjImJEY8cOWLvLlEV3333ndHPz8SJE+3dNarE1N+krVu32rtrDo11YomIiIhIdpgISERERESywyCWiIiIiGSHQSwRERERyQ6DWCIiIiKSHQaxRERERCQ7DGKJiIiISHYYxBIRERGR7DCIJSIiIiLZYRBLRERERLLDIJaIqI4mTZoEQRAMfiqvWV8X27ZtQ4MGDSTZV20dPXoUI0aMQJMmTSAIAr766iu79oeIiEEsEZEEhg4ditzcXL2fyMhIe3fLgFKprNXjSkpK0KVLF7z11lsS94iIqHYYxBIRScDLywuhoaF6P+7u7gCAPXv2oHv37vD29kZUVBSSk5Nx9+5d3WPXr1+PTp06wdfXF+Hh4Zg1axaKi4sBAIcPH8azzz6LwsJC3QjvsmXLAMDoiGiDBg2wbds2AMClS5cgCAJ27tyJAQMGwNvbGx9//DEAYOvWrWjfvj28vb3Rrl07bNq0qdrnN2zYMKxYsQJ/+9vfJHi1iIjqrp69O0BE5Mz27t2L8ePH41//+hf69u2LCxcu4LnnngMALF26FADg5uaGf/3rX2jRogWys7Mxa9YsLFy4EJs2bULv3r3xxhtvYMmSJTh37hwAwM/Pz6I+LFq0CK+//jq2bt0KLy8vvPPOO1i6dCneeustdOvWDT///DOmTZsGX19fTJw4UdoXgIjIShjEEhFJ4JtvvtELLocNG4bPP/8cK1euRFJSki44jIqKwquvvoqFCxfqgtg5c+boHhcZGYlXX30VM2fOxKZNm+Dp6YmAgAAIgoDQ0NBa9W3OnDl6I6ivvvoqXn/9dd22yMhIZGVlYfPmzQxiiUg2GMQSEUkgPj4eKSkputu+vr4AgJ9++gknTpzAypUrdfepVCqUlZWhtLQUCoUC3333Hf7xj38gKysLRUVFuHv3LsrKylBSUqLbT13Exsbq/v/GjRu4cuUKpkyZgmnTpum23717FwEBAXU+FhGRrTCIJSKSgK+vL1q1amWwXa1WIzk52Wguqbe3Ny5fvozhw4djxowZePXVVxEYGIgffvgBU6ZMqXESliAIEEVRb5uxx1QOhNVqNQDgnXfewQMPPKDXTpvDS0QkBwxiiYisKCYmBufOnTMa4ALAyZMncffuXbz++utwc9PMtd25c6deG09PT6hUKoPHNm7cGLm5ubrbv//+O0pLS6vtT0hICJo2bYqLFy/i6aeftvTpEBE5DAaxRERWtGTJEjzyyCMIDw/H448/Djc3N/zyyy/49ddfsWLFCrRs2RJ3797Fm2++iREjRuC///0v3n77bb19tGjRAsXFxTh48CC6dOkChUIBhUKBgQMH4q233kKvXr2gVquxaNEieHh41NinZcuW4cUXX4S/vz+GDRuG8vJynDx5En/++ScSExONPqa4uFiv7m12djYyMzMRGBiI5s2b1+1FIiKqBZbYIiKyoiFDhuCbb77B/v370aNHD/Tq1Qvr169HREQEAKBr165Yv3491qxZg44dO2L79u1YtWqV3j569+6NGTNm4Mknn0Tjxo2xdu1aAMDrr7+O8PBw9OvXD+PGjcP8+fOhUChq7NPUqVPx7rvvYtu2bejUqRP69++Pbdu2VVvX9uTJk+jWrRu6desGAEhMTES3bt2wZMmS2r40RER1IohVE6qIiIiIiBwcR2KJiIiISHYYxBIRERGR7DCIJSIiIiLZYRBLRERERLLDIJaIiIiIZIdBLBERERHJDoNYIiIiIpIdBrFEREREJDsMYomIiIhIdhjEEhEREZHsMIglIiIiItn5/wOsmKjqjl/rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KMEans 2nd Example\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate sample data\n",
    "np.random.seed(42)\n",
    "# Creating random data with two features\n",
    "data = np.random.randn(100, 2) * 3 + 2\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "# Finding the optimal number of clusters using the Elbow Method\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
    "    kmeans.fit(scaled_data)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "# Plotting the Elbow Method graph\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, 11), wcss, marker='o', linestyle='-', color='blue')\n",
    "plt.title('Elbow Method for Optimal k')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('WCSS')\n",
    "plt.xticks(np.arange(1, 11, 1))\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Choosing the optimal number of clusters (let's say 3) and fitting the final KMeans model\n",
    "optimal_clusters = 3\n",
    "final_kmeans = KMeans(n_clusters=optimal_clusters, init='k-means++', random_state=42)\n",
    "final_kmeans.fit(scaled_data)\n",
    "\n",
    "# Get cluster labels for each data point\n",
    "cluster_labels = final_kmeans.labels_\n",
    "\n",
    "# Plotting the clusters\n",
    "plt.figure(figsize=(8, 6))\n",
    "for cluster in range(optimal_clusters):\n",
    "    plt.scatter(scaled_data[cluster_labels == cluster, 0], scaled_data[cluster_labels == cluster, 1], label=f'Cluster {cluster + 1}')\n",
    "plt.scatter(final_kmeans.cluster_centers_[:, 0], final_kmeans.cluster_centers_[:, 1], s=200, c='red', label='Centroids', marker='X')\n",
    "plt.title('KMeans Clustering with Standardized Data')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c203cf59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of scaled_data: (100, 2)\n",
      "Shape of y_predict: (250,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of scaled_data:\", scaled_data.shape)\n",
    "print(\"Shape of y_predict:\", y_predict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa0488c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WCSS (Inertia): 80.97157524383526\n",
      "Silhouette Score: 0.3658276437359579\n",
      "Calinski-Harabasz Score: 71.29512527439074\n",
      "Davies-Bouldin Score: 0.8667082323076681\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score\n",
    "\n",
    "# Generating sample data\n",
    "np.random.seed(42)\n",
    "data = np.random.randn(100, 2) * 3 + 2  # Generating random data with two features\n",
    "\n",
    "# Standardizing the data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "# Performing KMeans clustering\n",
    "kmeans = KMeans(n_clusters=3, init='k-means++', random_state=42)\n",
    "y_predict = kmeans.fit_predict(scaled_data)\n",
    "\n",
    "# Calculating evaluation metrics\n",
    "inertia = kmeans.inertia_\n",
    "silhouette_avg = silhouette_score(scaled_data, y_predict)\n",
    "ch_score = calinski_harabasz_score(scaled_data, y_predict)\n",
    "db_score = davies_bouldin_score(scaled_data, y_predict)\n",
    "\n",
    "# Displaying the evaluation metrics\n",
    "print(\"WCSS (Inertia):\", inertia)\n",
    "print(\"Silhouette Score:\", silhouette_avg)\n",
    "print(\"Calinski-Harabasz Score:\", ch_score)\n",
    "print(\"Davies-Bouldin Score:\", db_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a32c7402",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\Faraz Ahmad\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WCSS (Inertia): 80.97157524383526\n",
      "Silhouette Score: 0.3658276437359579\n",
      "Calinski-Harabasz Score: 71.29512527439074\n",
      "Davies-Bouldin Score: 0.8667082323076681\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generating sample data\n",
    "np.random.seed(42)\n",
    "data = np.random.randn(100, 2) * 3 + 2  # Generating random data with two features\n",
    "\n",
    "# Standardizing the data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "# Finding the optimal number of clusters using the Elbow Method\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
    "    kmeans.fit(scaled_data)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "# Plotting the Elbow Method graph\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, 11), wcss, marker='o', linestyle='-', color='blue')\n",
    "plt.title('Elbow Method for Optimal k')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('WCSS')\n",
    "plt.xticks(np.arange(1, 11, 1))\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Performing KMeans clustering\n",
    "optimal_clusters = 3  # Choose the optimal number of clusters\n",
    "final_kmeans = KMeans(n_clusters=optimal_clusters, init='k-means++', random_state=42)\n",
    "final_kmeans.fit(scaled_data)\n",
    "\n",
    "# Get cluster labels for each data point\n",
    "cluster_labels = final_kmeans.labels_\n",
    "\n",
    "# Calculating evaluation metrics\n",
    "inertia = final_kmeans.inertia_\n",
    "silhouette_avg = silhouette_score(scaled_data, cluster_labels)\n",
    "ch_score = calinski_harabasz_score(scaled_data, cluster_labels)\n",
    "db_score = davies_bouldin_score(scaled_data, cluster_labels)\n",
    "\n",
    "# Displaying the evaluation metrics\n",
    "print(\"WCSS (Inertia):\", inertia)\n",
    "print(\"Silhouette Score:\", silhouette_avg)\n",
    "print(\"Calinski-Harabasz Score:\", ch_score)\n",
    "print(\"Davies-Bouldin Score:\", db_score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e9bbf1",
   "metadata": {},
   "source": [
    "Here's an interpretation of the results obtained from the KMeans clustering evaluation metrics:\n",
    "\n",
    "WCSS (Inertia): 80.97157524383526\n",
    "\n",
    "Interpretation: Within-Cluster Sum of Squares (WCSS) or inertia measures the sum of squared distances of samples to their assigned cluster centroids.\n",
    "Interpretation of Value: A lower WCSS indicates that the data points within each cluster are closer to the centroid, suggesting more compact and well-separated clusters. In this case, the value 80.97 suggests relatively tight clusters.\n",
    "Silhouette Score: 0.3658276437359579\n",
    "\n",
    "Interpretation: Silhouette Score measures how well-separated clusters are. It quantifies the average distance between clusters and the separation of clusters.\n",
    "Interpretation of Value: The silhouette score ranges between -1 to 1, where higher values indicate better-defined clusters. Here, the score of 0.366 suggests moderate separation and reasonable clustering structure.\n",
    "Calinski-Harabasz Score: 71.29512527439074\n",
    "\n",
    "Interpretation: The Calinski-Harabasz Index calculates the ratio of between-cluster dispersion to within-cluster dispersion. It evaluates how well-separated the clusters are.\n",
    "Interpretation of Value: A higher Calinski-Harabasz score implies better-defined, more separated clusters. The value of 71.30 indicates a reasonable cluster separation.\n",
    "Davies-Bouldin Score: 0.8667082323076681\n",
    "\n",
    "Interpretation: The Davies-Bouldin Index computes the average similarity between each cluster and its most similar one. It assesses the compactness and separability of clusters.\n",
    "Interpretation of Value: The index ranges from 0 to infinity; lower values denote better clustering. Here, the score of 0.867 suggests moderate overlap or similarity between clusters, but still indicates reasonable clustering structure.\n",
    "Overall Interpretation:\n",
    "\n",
    "The WCSS shows relatively compact clusters.\n",
    "Silhouette score, Calinski-Harabasz Index, and Davies-Bouldin Index collectively indicate moderate but reasonably defined clusters with some degree of overlap or similarity between clusters.\n",
    "These metrics provide a comprehensive evaluation of the clustering quality, suggesting a moderately well-structured clustering pattern in the data. Adjusting the number of clusters or exploring different clustering algorithms might further refine the clustering results."
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