diff --git a/bonus_1_prediction.csv b/bonus_1_prediction.csv
new file mode 100644
index 0000000..63e48e5
--- /dev/null
+++ b/bonus_1_prediction.csv
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diff --git a/bonus_1_predictions.csv b/bonus_1_predictions.csv
new file mode 100644
index 0000000..63e48e5
--- /dev/null
+++ b/bonus_1_predictions.csv
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diff --git a/bonus_2_predictions.csv b/bonus_2_predictions.csv
new file mode 100644
index 0000000..2f41508
--- /dev/null
+++ b/bonus_2_predictions.csv
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diff --git a/bonus_3_predictions.csv b/bonus_3_predictions.csv
new file mode 100644
index 0000000..a274259
--- /dev/null
+++ b/bonus_3_predictions.csv
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diff --git a/bonus_task_1.py b/bonus_task_1.py
new file mode 100644
index 0000000..02120ee
--- /dev/null
+++ b/bonus_task_1.py
@@ -0,0 +1,46 @@
+#! /usr/bin/env python3
+import pandas as pd
+import numpy as np
+from sklearn import *
+
+## Training
+train_descriptors = pd.read_csv("../train_and_test_sets/train_descriptors.csv")
+train_mord3d = pd.read_csv("../train_and_test_sets/train_mord3d.csv")
+train_morgan = pd.read_csv("../train_and_test_sets/train_morgan.csv")
+train_rdk = pd.read_csv("../train_and_test_sets/train_rdk.csv")
+
+## Train responses
+train_crystals = pd.read_csv("../train_and_test_sets/train_crystals.csv")
+train_distances = pd.read_csv("../train_and_test_sets/train_distances.csv")
+train_centroid_distances = pd.read_csv("../train_and_test_sets/train_centroid_distances.csv")
+
+train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis=1, how="any")
+train_mord3d_full = train_mord3d.dropna(axis=1, how="any").drop(['identifiers','Unnamed: 0', 'InchiKey', 
+                                                                 'smiles', 'name'], axis=1)
+train_morgan_full = train_morgan.drop('0',axis=1)
+train_rdk = train_rdk.drop('0',axis=1)
+
+## Testing
+test_descriptors = pd.read_csv("../train_and_test_sets/test_descriptors.csv")
+test_mord3d = pd.read_csv("../train_and_test_sets/test_mord3d.csv")
+test_morgan = pd.read_csv("../train_and_test_sets/test_morgan.csv")
+test_rdk = pd.read_csv("../train_and_test_sets/test_rdk.csv")
+
+test_descriptors_full = test_descriptors[train_descriptors_full.columns]
+test_mord3d_full = test_mord3d[train_mord3d_full.columns]
+test_morgan_full = test_morgan.drop('0',axis=1)
+test_rdk = test_rdk.drop('0',axis=1)
+
+train_PCA = decomposition.PCA(n_components=.95)
+scaler_for_PCA = preprocessing.StandardScaler()
+train_descriptors_PCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(train_descriptors_full))
+test_descriptors_PCA = train_PCA.transform(scaler_for_PCA.transform(test_descriptors_full))
+
+target = 'packing_coefficient'
+
+from sklearn import tree
+model = tree.DecisionTreeRegressor(criterion='mae')
+model.fit(train_descriptors_PCA, train_crystals[target])
+
+predictions = model.predict(test_descriptors_PCA)
+pd.DataFrame(predictions).to_csv("task_packing_coefficient_predictions22.csv", header=False, index=False)
diff --git a/bonus_task_2.ipynb b/bonus_task_2.ipynb
new file mode 100644
index 0000000..45be519
--- /dev/null
+++ b/bonus_task_2.ipynb
@@ -0,0 +1,456 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "available-acquisition",
+   "metadata": {},
+   "source": [
+    "# FoNS Datathon 2021 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "acknowledged-microphone",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "plt.rcParams['figure.figsize'] = (8,6)\n",
+    "import seaborn as sns\n",
+    "from tqdm.notebook import tqdm\n",
+    "from sklearn import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "union-malpractice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_descriptors = pd.read_csv(\"train_descriptors.csv\")\n",
+    "train_mord3d = pd.read_csv(\"train_mord3d.csv\")\n",
+    "train_morgan = pd.read_csv(\"train_morgan.csv\")\n",
+    "train_rdk = pd.read_csv(\"train_rdk.csv\")\n",
+    "\n",
+    "train_crystals = pd.read_csv(\"train_crystals.csv\")\n",
+    "train_distances = pd.read_csv(\"train_distances.csv\")\n",
+    "train_centroid_distances = pd.read_csv(\"train_centroid_distances.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "electrical-climate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_descriptors = pd.read_csv(\"test_descriptors.csv\")\n",
+    "test_mord3d = pd.read_csv(\"test_mord3d.csv\")\n",
+    "test_morgan = pd.read_csv(\"test_morgan.csv\")\n",
+    "test_rdk = pd.read_csv(\"test_rdk.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demonstrated-arizona",
+   "metadata": {},
+   "source": [
+    "### Data pre-processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "normal-marketing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13449, 984)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis= 1, how=\"any\")\n",
+    "train_descriptors_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "central-commonwealth",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3363, 984)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_descriptors_full = test_descriptors[train_descriptors_full.columns]\n",
+    "test_descriptors_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "joint-champion",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(13449, 124) (3363, 124)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_PCA = decomposition.PCA(n_components=.95)\n",
+    "scaler_for_PCA = preprocessing.StandardScaler()\n",
+    "train_descriptors_PCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(train_descriptors_full))\n",
+    "test_descriptors_PCA = train_PCA.transform(scaler_for_PCA.transform(test_descriptors_full))\n",
+    "print(train_descriptors_PCA.shape, test_descriptors_PCA.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dying-african",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch._C.Generator at 0x7f717d185770>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.manual_seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "happy-watson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.tensor(train_descriptors_PCA).float()\n",
+    "target = 'cell_volume'\n",
+    "y = torch.tensor(train_crystals[target].values).float()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "individual-necessity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d_in = x.shape[1]\n",
+    "d_out = y.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "pleased-latino",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = y.reshape(d_out,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "particular-triumph",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([3363, 984])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test = torch.tensor(test_descriptors_full.values).float()\n",
+    "x_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "quarterly-estonia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_pca = torch.nn.Sequential(\n",
+    "        torch.nn.Linear(d_in,2*d_in),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(2*d_in,1),\n",
+    "        torch.nn.ReLU())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "thrown-coverage",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e83280f6b364894a4367a390e50ccfb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99 250.53619384765625\n",
+      "199 235.1154327392578\n",
+      "299 229.72683715820312\n",
+      "399 226.78500366210938\n",
+      "499 224.87969970703125\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss_fn = torch.nn.L1Loss()\n",
+    "learning_rate = 1e-2\n",
+    "optimizer = torch.optim.Adam(model_pca.parameters(),lr=learning_rate)\n",
+    "for t in tqdm(range(500)):\n",
+    "    # Forward pass: compute predicted y by passing x to the model\n",
+    "    y_pred = model_pca(x_pca)\n",
+    "    \n",
+    "    # compute and print loss\n",
+    "    loss = loss_fn(y_pred, y)\n",
+    "    if t % 100 == 99:\n",
+    "        print(t, loss.item())\n",
+    "    \n",
+    "    # Before the backward pass, use the optimizer object to zero all of the\n",
+    "    # gradients for the variables it will update (which are the learnable\n",
+    "    # weights of the model). This is because by default, gradients are\n",
+    "    # accumulated in buffers( i.e, not overwritten) whenever .backward()\n",
+    "    # is called. Checkout docs of torch.autograd.backward for more details.\n",
+    "    optimizer.zero_grad()\n",
+    "    \n",
+    "    # Backward pass: compute gradient of the loss with respect to model\n",
+    "    # parameters\n",
+    "    loss.backward()\n",
+    "    \n",
+    "    # Calling the step function on an Optimizer makes an update to its\n",
+    "    # parameters\n",
+    "    optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "scenic-samba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46c68056fab14b71bde34761e1104cf7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99 223.65011596679688\n",
+      "199 222.5361328125\n",
+      "299 221.5463409423828\n",
+      "399 220.76123046875\n",
+      "499 219.95713806152344\n",
+      "599 219.3131866455078\n",
+      "699 218.7624969482422\n",
+      "799 218.2318572998047\n",
+      "899 217.77935791015625\n",
+      "999 217.29345703125\n"
+     ]
+    }
+   ],
+   "source": [
+    "learning_rate = 1e-2\n",
+    "optimizer = torch.optim.Adam(model_pca.parameters(),lr=learning_rate)\n",
+    "for t in tqdm(range(1000)):\n",
+    "    # Forward pass: compute predicted y by passing x to the model\n",
+    "    y_pred = model_pca(x_pca)\n",
+    "    \n",
+    "    # compute and print loss\n",
+    "    loss = loss_fn(y_pred, y)\n",
+    "    if t % 100 == 99:\n",
+    "        print(t, loss.item())\n",
+    "    \n",
+    "    # Before the backward pass, use the optimizer object to zero all of the\n",
+    "    # gradients for the variables it will update (which are the learnable\n",
+    "    # weights of the model). This is because by default, gradients are\n",
+    "    # accumulated in buffers( i.e, not overwritten) whenever .backward()\n",
+    "    # is called. Checkout docs of torch.autograd.backward for more details.\n",
+    "    optimizer.zero_grad()\n",
+    "    \n",
+    "    # Backward pass: compute gradient of the loss with respect to model\n",
+    "    # parameters\n",
+    "    loss.backward()\n",
+    "    \n",
+    "    # Calling the step function on an Optimizer makes an update to its\n",
+    "    # parameters\n",
+    "    optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "tired-concept",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fc8253e4fc248e69d5f70883d723ec8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99 216.90133666992188\n",
+      "199 216.56312561035156\n",
+      "299 216.06517028808594\n",
+      "399 215.7594451904297\n",
+      "499 215.3126678466797\n",
+      "599 214.90951538085938\n",
+      "699 214.51931762695312\n",
+      "799 214.1055908203125\n",
+      "899 213.79388427734375\n",
+      "999 213.3924102783203\n"
+     ]
+    }
+   ],
+   "source": [
+    "learning_rate = 1e-2\n",
+    "optimizer = torch.optim.Adam(model_pca.parameters(),lr=learning_rate)\n",
+    "for t in tqdm(range(1000)):\n",
+    "    # Forward pass: compute predicted y by passing x to the model\n",
+    "    y_pred = model_pca(x_pca)\n",
+    "    \n",
+    "    # compute and print loss\n",
+    "    loss = loss_fn(y_pred, y)\n",
+    "    if t % 100 == 99:\n",
+    "        print(t, loss.item())\n",
+    "    \n",
+    "    # Before the backward pass, use the optimizer object to zero all of the\n",
+    "    # gradients for the variables it will update (which are the learnable\n",
+    "    # weights of the model). This is because by default, gradients are\n",
+    "    # accumulated in buffers( i.e, not overwritten) whenever .backward()\n",
+    "    # is called. Checkout docs of torch.autograd.backward for more details.\n",
+    "    optimizer.zero_grad()\n",
+    "    \n",
+    "    # Backward pass: compute gradient of the loss with respect to model\n",
+    "    # parameters\n",
+    "    loss.backward()\n",
+    "    \n",
+    "    # Calling the step function on an Optimizer makes an update to its\n",
+    "    # parameters\n",
+    "    optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "practical-grass",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_pca = torch.tensor(test_descriptors_PCA).float()\n",
+    "predictions_pca = model_pca(x_test_pca).flatten().detach().numpy()\n",
+    "np.savetxt(\"bonus_2_predictions.csv\", predictions_pca)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "certain-magazine",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bonus_task_3.py b/bonus_task_3.py
new file mode 100644
index 0000000..26ffc54
--- /dev/null
+++ b/bonus_task_3.py
@@ -0,0 +1,45 @@
+#! /usr/bin/env python3
+import pandas as pd
+import numpy as np
+from sklearn import *
+
+## Training
+train_descriptors = pd.read_csv("../train_and_test_sets/train_descriptors.csv")
+train_mord3d = pd.read_csv("../train_and_test_sets/train_mord3d.csv")
+train_morgan = pd.read_csv("../train_and_test_sets/train_morgan.csv")
+train_rdk = pd.read_csv("../train_and_test_sets/train_rdk.csv")
+
+## Train responses
+train_crystals = pd.read_csv("../train_and_test_sets/train_crystals.csv")
+train_distances = pd.read_csv("../train_and_test_sets/train_distances.csv")
+train_centroid_distances = pd.read_csv("../train_and_test_sets/train_centroid_distances.csv")
+
+train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis=1, how="any")
+train_mord3d_full = train_mord3d.dropna(axis=1, how="any").drop(['identifiers','Unnamed: 0', 'InchiKey', 
+                                                                 'smiles', 'name'], axis=1)
+train_morgan_full = train_morgan.drop('0',axis=1)
+train_rdk = train_rdk.drop('0',axis=1)
+
+## Testing
+test_descriptors = pd.read_csv("../train_and_test_sets/test_descriptors.csv")
+test_mord3d = pd.read_csv("../train_and_test_sets/test_mord3d.csv")
+test_morgan = pd.read_csv("../train_and_test_sets/test_morgan.csv")
+test_rdk = pd.read_csv("../train_and_test_sets/test_rdk.csv")
+
+test_descriptors_full = test_descriptors[train_descriptors_full.columns]
+test_mord3d_full = test_mord3d[train_mord3d_full.columns]
+test_morgan_full = test_morgan.drop('0',axis=1)
+test_rdk = test_rdk.drop('0',axis=1)
+
+target = 'spacegroup_symbol'
+
+from sklearn.ensemble import RandomForestClassifier
+clf = RandomForestClassifier(random_state=0, n_estimators=10)
+clf.fit(train_data, train_crystals[target])
+
+predictions = clf.predict(train_data)
+
+f1_score(predictions, train_crystals[target], average='macro')
+
+predictions = clf.predict(test_data)
+pd.DataFrame(predictions).to_csv("task_spacegroup_symbol_random_forestt.csv", header=False, index=False)
diff --git a/task_1.py b/task_1.py
new file mode 100644
index 0000000..73791b0
--- /dev/null
+++ b/task_1.py
@@ -0,0 +1,59 @@
+
+
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import *
+
+from sklearn.metrics import mean_absolute_error
+from sklearn.feature_selection import VarianceThreshold
+
+np.random.seed(10)
+
+## Source data
+
+data_folder = 'train_and_test_sets/'
+
+
+train_descriptors = pd.read_csv(data_folder + "train_descriptors.csv")
+train_mord3d = pd.read_csv(data_folder + "train_mord3d.csv")
+train_morgan = pd.read_csv(data_folder + "train_morgan.csv")
+train_rdk = pd.read_csv(data_folder + "train_rdk.csv")
+
+train_crystals = pd.read_csv(data_folder + "train_crystals.csv")
+
+
+test_descriptors = pd.read_csv(data_folder + "test_descriptors.csv")
+test_mord3d = pd.read_csv(data_folder + "test_mord3d.csv")
+test_morgan = pd.read_csv(data_folder + "test_morgan.csv")
+test_rdk = pd.read_csv(data_folder + "test_rdk.csv")
+
+
+
+
+## Data pre-processing
+
+train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis=1, how="any")
+
+test_descriptors_full = test_descriptors[train_descriptors_full.columns]
+
+
+
+
+
+## Lasso with CV
+
+reg_lasso_cv = linear_model.LassoCV(cv=5, normalize=True)
+
+reg_lasso_cv.fit(train_descriptors_full, train_crystals['calculated_density'])
+
+
+
+predictions = reg_lasso_cv.predict(test_descriptors_full)
+
+np.savetxt("task_1_predictions_final.csv", predictions)
+
+
+
+
diff --git a/task_1_predictions.csv b/task_1_predictions.csv
new file mode 100644
index 0000000..d67d1f5
--- /dev/null
+++ b/task_1_predictions.csv
@@ -0,0 +1,3363 @@
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diff --git a/task_2_predictions.csv b/task_2_predictions.csv
new file mode 100644
index 0000000..3e974f7
--- /dev/null
+++ b/task_2_predictions.csv
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diff --git a/task_2_script.py b/task_2_script.py
new file mode 100644
index 0000000..87583bb
--- /dev/null
+++ b/task_2_script.py
@@ -0,0 +1,254 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import *
+
+
+# Basic
+import numpy as np
+import pandas as pd
+# Models
+from sklearn import svm
+from sklearn.svm import LinearSVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.neural_network import MLPClassifier
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import GaussianNB
+# Extras
+from sklearn.decomposition import PCA
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import precision_recall_fscore_support
+from sklearn.metrics import accuracy_score
+
+# Scrap
+import pylab
+from scipy.linalg import eigh, svd, qr, solve
+from scipy.sparse import eye, csr_matrix
+from scipy.sparse.linalg import eigsh
+import sys
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.utils import check_random_state, check_array
+from sklearn.utils.extmath import stable_cumsum
+from sklearn.utils.validation import check_is_fitted, FLOAT_DTYPES
+from sklearn.neighbors import NearestNeighbors
+import math
+from sklearn.cross_decomposition import CCA
+#from sklearn.preprocessing import StandardScaler
+#import matplotlib
+import matplotlib.pyplot as plt
+
+
+pd.options.display.max_columns = None
+pd.options.display.max_rows = None
+
+# Models
+def all_models(train_data, train_lbl, test_data): # ,test_lbl):
+    ## LINEAR SVM
+    # set up model
+    # Scaler and regression
+    pipe_linearSVM = make_pipeline(StandardScaler(), LinearSVC(tol=1e-5))
+    # train
+    pipe_linearSVM.fit(train_data, train_lbl)
+    # predict
+    pred_test_linearSVM = pipe_linearSVM.predict(test_data)
+    # accuracy
+#    acc_test_linearSVM = pipe_linearSVM.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_linearSVM, rec_linearSVM, f1_linearSVM, sup_linearSVM = precision_recall_fscore_support(test_lbl, pred_test_linearSVM, average='weighted')
+#    acc_linearSVM = accuracy_score(test_lbl, pred_test_linearSVM)
+
+    ## LOGISTIC REGRESSION
+    # set up model
+    # Scaler and regression
+    pipe_LogisticR = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
+    # train
+    pipe_LogisticR.fit(train_data, train_lbl)
+    # predict
+    pred_test_LogisticR = pipe_LogisticR.predict(test_data)
+    # accuracy
+#    acc_test_LogisticR = pipe_LogisticR.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_LogisticR, rec_LogisticR, f1_LogisticR, sup_LogisticR = precision_recall_fscore_support(test_lbl, pred_test_LogisticR, average='weighted')
+#    acc_LogisticR = accuracy_score(test_lbl, pred_test_LogisticR)
+
+    ## RANDOM FOREST
+    # set up model
+    # Scaler and regression
+    pipe_RF = make_pipeline(StandardScaler(), RandomForestClassifier(max_depth=2, random_state=0))
+    # train
+    pipe_RF.fit(train_data, train_lbl)
+    # predict
+    pred_test_RF = pipe_RF.predict(test_data)
+    # accuracy
+#    acc_test_RF = pipe_RF.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_RF, rec_RF, f1_RF, sup_RF = precision_recall_fscore_support(test_lbl, pred_test_RF, average='weighted')
+#    acc_RF = accuracy_score(test_lbl, pred_test_RF)
+
+    ## NEURAL NETWORK
+    # set up model
+    # Scaler and regression
+    pipe_NN = make_pipeline(StandardScaler(), MLPClassifier(max_iter=100))
+    # train
+    pipe_NN.fit(train_data, train_lbl)
+    # predict
+    pred_test_NN = pipe_NN.predict(test_data)
+    # accuracy
+#    acc_test_NN = pipe_NN.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_NN, rec_NN, f1_NN, sup_NN = precision_recall_fscore_support(test_lbl, pred_test_NN, average='weighted')
+#    acc_NN = accuracy_score(test_lbl, pred_test_NN)
+
+    ## SVM w/RBF KERNEL (SVC)
+    # set up model
+    # Scaler and regression
+    pipe_SVC = make_pipeline(StandardScaler(), SVC(kernel = 'rbf', gamma= 'auto'))
+    # train
+    pipe_SVC.fit(train_data, train_lbl)
+    # predict
+    pred_test_SVC = pipe_SVC.predict(test_data)
+    # accuracy
+#    acc_test_SVC = pipe_SVC.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_SVC, rec_SVC, f1_SVC, sup_SVC = precision_recall_fscore_support(test_lbl, pred_test_SVC, average='weighted')
+#    acc_SVC = accuracy_score(test_lbl, pred_test_SVC)
+
+    ## DECISION TREES
+    # set up model
+    # Scaler and regression
+    pipe_DecisionTrees = make_pipeline(StandardScaler(), DecisionTreeClassifier(random_state=0))
+    # train
+    pipe_DecisionTrees.fit(train_data, train_lbl)
+    # predict
+    pred_test_DecisionTrees = pipe_DecisionTrees.predict(test_data)
+    # accuracy
+#    acc_test_DecisionTrees = pipe_DecisionTrees.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_DecisionTrees, rec_DecisionTrees, f1_DecisionTrees, sup_DecisionTrees = precision_recall_fscore_support(test_lbl, pred_test_DecisionTrees, average='weighted')
+#    acc_DecisionTrees = accuracy_score(test_lbl, pred_test_DecisionTrees)
+
+    ## DECISION TREES w/ ADABOOST
+    # set up model
+    # Scaler and regression
+    pipe_DT_AdaBoost = make_pipeline(StandardScaler(), AdaBoostClassifier())
+    # train
+    pipe_DT_AdaBoost.fit(train_data, train_lbl)
+    # predict
+    pred_test_DT_AdaBoost = pipe_DT_AdaBoost.predict(test_data)
+    # accuracy
+#    acc_test_DT_AdaBoost = pipe_DT_AdaBoost.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_DT_AdaBoost, rec_DT_AdaBoost, f1_DT_AdaBoost, sup_DT_AdaBoost = precision_recall_fscore_support(test_lbl, pred_test_DT_AdaBoost, average='weighted')
+#    acc_DT_AdaBoost = accuracy_score(test_lbl, pred_test_DT_AdaBoost)
+
+    ## LDA
+    # set up model
+    # Scaler and regression
+    pipe_LDA = make_pipeline(StandardScaler(), LinearDiscriminantAnalysis())
+    # train
+    pipe_LDA.fit(train_data, train_lbl)
+    # predict
+    pred_test_LDA = pipe_LDA.predict(test_data)
+    # accuracy
+#    acc_test_LDA = pipe_LDA.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_LDA, rec_LDA, f1_LDA, sup_LDA = precision_recall_fscore_support(test_lbl, pred_test_LDA, average='weighted')
+#    acc_LDA = accuracy_score(test_lbl, pred_test_LDA)
+
+    # K-NN
+    # set up model
+    # Scaler and regression
+    pipe_KNN = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=10))
+    # train
+    pipe_KNN.fit(train_data, train_lbl)
+    # predict
+    pred_test_KNN = pipe_KNN.predict(test_data)
+    # accuracy
+#    acc_test_KNN = pipe_KNN.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_KNN, rec_KNN, f1_KNN, sup_KNN = precision_recall_fscore_support(test_lbl, pred_test_KNN, average='weighted')
+#    acc_KNN = accuracy_score(test_lbl, pred_test_KNN)
+
+    ## NAIVE BAYES
+    # set up model
+    # Scaler and regression
+    pipe_NaiveBayes = make_pipeline(StandardScaler(), GaussianNB())
+    # train
+    pipe_NaiveBayes.fit(train_data, train_lbl)
+    # predict
+    pred_test_NaiveBayes = pipe_NaiveBayes.predict(test_data)
+    # accuracy
+#    acc_test_NaiveBayes = pipe_NaiveBayes.score(test_data, test_lbl)
+#    # other evaluation measures
+#    prec_NB, rec_NB, f1_NB, sup_NB = precision_recall_fscore_support(test_lbl, pred_test_NaiveBayes, average='weighted')
+#    acc_NB = accuracy_score(test_lbl, pred_test_NaiveBayes)
+    # Overall scores
+
+    return pred_test_linearSVM, pred_test_LogisticR, pred_test_RF, pred_test_NN,pred_test_SVC, pred_test_DecisionTrees, pred_test_DT_AdaBoost, pred_test_LDA, pred_test_KNN, pred_test_NaiveBayes
+
+
+train_descriptors = pd.read_csv("train_and_test_sets/train_descriptors.csv")
+train_mord3d = pd.read_csv("train_and_test_sets/train_mord3d.csv")
+train_morgan = pd.read_csv("train_and_test_sets/train_morgan.csv")
+train_rdk = pd.read_csv("train_and_test_sets/train_rdk.csv")
+
+train_crystals = pd.read_csv("train_and_test_sets/train_crystals.csv")
+train_distances = pd.read_csv("train_and_test_sets/train_distances.csv")
+train_centroid_distances = pd.read_csv("train_and_test_sets/train_centroid_distances.csv")
+
+test_descriptors = pd.read_csv("train_and_test_sets/test_descriptors.csv")
+test_mord3d = pd.read_csv("train_and_test_sets/test_mord3d.csv")
+test_morgan = pd.read_csv("train_and_test_sets/test_morgan.csv")
+test_rdk = pd.read_csv("train_and_test_sets/test_rdk.csv")
+
+# Data pre-processing
+train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis= 1, how="any")
+train_descriptors_full.shape
+
+train_mord3d_full = train_mord3d.iloc[:, 3:-2].dropna(axis= 1, how="any")
+print(train_mord3d_full.shape)
+train_morgan_full = train_morgan.iloc[:, 3:-2].dropna(axis= 1, how="any")
+print(train_morgan_full.shape)
+train_rdk_full = train_morgan.iloc[:, 3:-2].dropna(axis= 1, how="any")
+print(train_rdk_full.shape)
+
+test_descriptors_full = test_descriptors[train_descriptors_full.columns]
+print(test_descriptors_full.shape)
+test_mord3d_full = test_mord3d[train_mord3d_full.columns]
+print(test_mord3d_full.shape)
+test_morgan_full = test_morgan[train_morgan_full.columns]
+print(test_morgan_full.shape)
+test_rdk_full = test_rdk[train_rdk_full.columns]
+print(test_rdk_full.shape)
+
+# Response
+is_crystal = train_crystals.loc[:,'is_centrosymmetric']
+
+# PCA
+# Select data
+train_PCA = decomposition.PCA(n_components=.95)
+scaler_for_PCA = preprocessing.StandardScaler()
+train_data_PCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(train_descriptors_full))
+test_data_PCA = train_PCA.transform(scaler_for_PCA.transform(test_descriptors_full))
+print(train_data_PCA.shape, test_data_PCA.shape)
+
+# all parameters not specified are set to their defaults
+# default solver is incredibly slow which is why it was changed to 'lbfgs'
+train_data = train_data_PCA
+train_lbl = is_crystal
+test_data = test_data_PCA
+
+
+pred_linearSVM, pred_LogisticR, pred_RF, pred_NN, pred_SVC, pred_DT, pred_AdaBoost, pred_LDA, pred_KNN, pred_NB = all_models(train_data = train_data_PCA,
+                         train_lbl = train_lbl,
+                         test_data = test_data_PCA)
+
+# Save for a selected model
+# example pred_linearSVM
+pd.DataFrame(pred_linearSVM).to_csv("task_2_predictions.csv")
diff --git a/task_3.ipynb b/task_3.ipynb
new file mode 100644
index 0000000..f3feec2
--- /dev/null
+++ b/task_3.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "planned-prayer",
+   "metadata": {},
+   "source": [
+    "# FoNS Datathon 2021 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "logical-instrumentation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "plt.rcParams['figure.figsize'] = (8,6)\n",
+    "import seaborn as sns\n",
+    "from sklearn import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "steady-protocol",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_descriptors = pd.read_csv(\"train_descriptors.csv\")\n",
+    "train_mord3d = pd.read_csv(\"train_mord3d.csv\")\n",
+    "train_morgan = pd.read_csv(\"train_morgan.csv\")\n",
+    "train_rdk = pd.read_csv(\"train_rdk.csv\")\n",
+    "\n",
+    "train_crystals = pd.read_csv(\"train_crystals.csv\")\n",
+    "train_distances = pd.read_csv(\"train_distances.csv\")\n",
+    "train_centroid_distances = pd.read_csv(\"train_centroid_distances.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "local-arrival",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_descriptors = pd.read_csv(\"test_descriptors.csv\")\n",
+    "test_mord3d = pd.read_csv(\"test_mord3d.csv\")\n",
+    "test_morgan = pd.read_csv(\"test_morgan.csv\")\n",
+    "test_rdk = pd.read_csv(\"test_rdk.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "formed-stand",
+   "metadata": {},
+   "source": [
+    "### Data pre-processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "boolean-modern",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13449, 984)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis= 1, how=\"any\")\n",
+    "train_descriptors_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "varied-pilot",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3363, 984)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_descriptors_full = test_descriptors[train_descriptors_full.columns]\n",
+    "test_descriptors_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "published-client",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(13449, 124) (3363, 124)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_PCA = decomposition.PCA(n_components=.95)\n",
+    "scaler_for_PCA = preprocessing.StandardScaler()\n",
+    "train_descriptors_PCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(train_descriptors_full))\n",
+    "test_descriptors_PCA = train_PCA.transform(scaler_for_PCA.transform(test_descriptors_full))\n",
+    "print(train_descriptors_PCA.shape, test_descriptors_PCA.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sunset-nature",
+   "metadata": {},
+   "source": [
+    "### Initial attempt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "legislative-nickname",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"mean\"\n",
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(train_descriptors_PCA, train_centroid_distances[target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bronze-baker",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "predictions = model.predict(train_descriptors_PCA)\n",
+    "plt.plot(train_centroid_distances[target], predictions, \"bo\")\n",
+    "plt.xlabel(\"True values\")\n",
+    "plt.ylabel(\"Predicted values\")\n",
+    "plt.title(\"Predicted z-value against true values\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "waiting-airfare",
+   "metadata": {},
+   "source": [
+    "### Cross-validation to evaluate the model's performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "raised-taylor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average negative MAE: -0.294\n",
+      "Standard deviation: 0.007\n"
+     ]
+    }
+   ],
+   "source": [
+    "cv_scores = model_selection.cross_val_score(\n",
+    "    model, \n",
+    "    train_descriptors_PCA, \n",
+    "    train_centroid_distances[target], \n",
+    "    scoring=\"neg_mean_absolute_error\")\n",
+    "\n",
+    "print(\"Average negative MAE:\", round(np.mean(cv_scores), 3))\n",
+    "print(\"Standard deviation:\", round(np.std(cv_scores), 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reduced-apache",
+   "metadata": {},
+   "source": [
+    "### Outputting predictions for automated checking \n",
+    "Link: https://github.com/stevenkbennett/fons_datathon_testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "comparative-mechanism",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(test_descriptors_PCA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "quick-vertical",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"my_task_3_predictions.csv\", predictions)\n",
+    "# Alternatively, if using pandas, you can use the following:\n",
+    "#pd.DataFrame(predictions).to_csv(\"task_x_predictions.csv\", header=False, index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/task_3_predictions.csv b/task_3_predictions.csv
new file mode 100644
index 0000000..cd7f31b
--- /dev/null
+++ b/task_3_predictions.csv
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diff --git a/task_3_predictions_nn.csv b/task_3_predictions_nn.csv
new file mode 100644
index 0000000..7a8e5a7
--- /dev/null
+++ b/task_3_predictions_nn.csv
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diff --git a/task_4.py b/task_4.py
new file mode 100644
index 0000000..7d18552
--- /dev/null
+++ b/task_4.py
@@ -0,0 +1,49 @@
+#! /usr/bin/env python3
+import pandas as pd
+import numpy as np
+from sklearn import *
+
+## Training
+train_descriptors = pd.read_csv("../train_and_test_sets/train_descriptors.csv")
+train_mord3d = pd.read_csv("../train_and_test_sets/train_mord3d.csv")
+train_morgan = pd.read_csv("../train_and_test_sets/train_morgan.csv")
+train_rdk = pd.read_csv("../train_and_test_sets/train_rdk.csv")
+
+## Train responses
+train_crystals = pd.read_csv("../train_and_test_sets/train_crystals.csv")
+train_distances = pd.read_csv("../train_and_test_sets/train_distances.csv")
+train_centroid_distances = pd.read_csv("../train_and_test_sets/train_centroid_distances.csv")
+
+train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis=1, how="any")
+train_mord3d_full = train_mord3d.dropna(axis=1, how="any").drop(['identifiers','Unnamed: 0', 'InchiKey', 
+                                                                 'smiles', 'name'], axis=1)
+train_morgan_full = train_morgan.drop('0',axis=1)
+train_rdk = train_rdk.drop('0',axis=1)
+
+## Testing
+test_descriptors = pd.read_csv("../train_and_test_sets/test_descriptors.csv")
+test_mord3d = pd.read_csv("../train_and_test_sets/test_mord3d.csv")
+test_morgan = pd.read_csv("../train_and_test_sets/test_morgan.csv")
+test_rdk = pd.read_csv("../train_and_test_sets/test_rdk.csv")
+
+test_descriptors_full = test_descriptors[train_descriptors_full.columns]
+test_mord3d_full = test_mord3d[train_mord3d_full.columns]
+test_morgan_full = test_morgan.drop('0',axis=1)
+test_rdk = test_rdk.drop('0',axis=1)
+
+train_PCA = decomposition.PCA(n_components=.95)
+scaler_for_PCA = preprocessing.StandardScaler()
+train_descriptors_PCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(train_descriptors_full))
+test_descriptors_PCA = train_PCA.transform(scaler_for_PCA.transform(test_descriptors_full))
+
+target = 'n_vdw_contacts'
+
+from sklearn import tree
+model = tree.DecisionTreeRegressor()
+train_data = pd.concat([train_descriptors_full, train_mord3d_full, train_morgan_full, train_rdk], axis=1)
+model.fit(train_data, train_distances[target])
+
+test_data = pd.concat([test_descriptors_full, test_mord3d_full, test_morgan_full, test_rdk], axis=1)
+predictions = model.predict(test_data)
+
+pd.DataFrame(predictions).to_csv("task_4_predictions_fspp.csv", header=False, index=False)
\ No newline at end of file
diff --git a/task_4_predictions.csv b/task_4_predictions.csv
new file mode 100644
index 0000000..4aadc0d
--- /dev/null
+++ b/task_4_predictions.csv
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diff --git a/test.txt b/test.txt
new file mode 100644
index 0000000..45b983b
--- /dev/null
+++ b/test.txt
@@ -0,0 +1 @@
+hi