Hi, I got a dataset and I want you to implement the following on the dataset named as 'dataset.csv'.  Preprocessing steps to implement:

Replace any missing or invalid values: In the given dataset, there are some missing or invalid values represented by letters like 'f', 'd', 'e', 'c', and 'a'. You should replace these values with appropriate values like the mean or median of the column, or you can remove the rows that contain these values.

Convert all values to numerical format: Some columns in the dataset contain string values. You need to convert them to numerical format using appropriate techniques like one-hot encoding use OneHotEncoder.

Scale the data: • Scale numerical variables to have zero mean and unit variance.You can use StandardScaler from sklearn or Normalize from PyTorch. Since the columns in the dataset have different ranges of values, you need to scale the data to ensure that all columns have the same range of values. You can use techniques like min-max scaling or standardization for this purpose.

Split the dataset into training and testing sets: You should split the dataset into two parts: a training set and a testing set. The training set is used to train the model, while the testing set is used to evaluate the performance of the model on new data.

Normalize the data: If you are using certain machine learning algorithms like neural networks, you may need to normalize the data to ensure that the features have similar variances. Use Pytorch

Check for outliers: You should check for outliers in the dataset and remove them if necessary. Outliers can have a significant impact on the performance of the model.
Dataset is given as header = ['f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'target']                                                                                                                                                                                                           1	108	60	46	178	35.5	0.415	0
5	97	76	27	0	35.6	0.378	1
4	83	86	19	0	29.3	0.317	0
1	114	66	36	200	38.1	0.289	0
1	149	68	29	127	29.3	0.349	1
5	117	86	30	105	39.1	0.251	0
1	111	94	0	0	32.8	0.265	0
4	112	78	40	0	39.4	0.236	0
1	116	78	29	180	36.1	0.496	0
0	141	84	26	0	32.4	0.433	0
2	175	88	0	0	22.9	0.326	0
2	92	52	0	0	30.1	0.141	0
3	130	78	23	79	28.4	0.323	1
8	120	86	0	0	28.4	0.259	1
2	174	88	37	120	44.5	0.646	1
2	106	56	27	165	29	0.426	0
2	105	75	0	0	23.3	0.56	0
4	95	60	32	0	35.4	0.284	0
0	126	86	27	120	27.4	0.515	0
8	65	72	23	0	32	0.6	0
2	99	60	17	160	36.6	0.453	0
1	102	74	0	0	39.5	0.293	1
11	120	80	37	150	42.3	0.785	1
3	102	44	20	94	30.8	0.4	0
1	109	58	18	116	28.5	0.219	0
9	140	94	0	0	32.7	0.734	1
13	153	88	37	140	40.6	1.174	0
12	100	84	33	105	30	0.488	0
1	147	94	41	0	49.3	0.358	1
1	81	74	41	57	46.3	1.096	0
3	187	70	22	200	36.4	0.408	1
6	162	62	0	0	24.3	0.178	1
4	136	70	0	0	31.2	1.182	1
1	121	78	39	74	39	0.261	0
3	108	62	24	0	26	0.223	0
0	181	88	44	510	43.3	0.222	1
8	154	78	32	0	32.4	0.443	1
1	128	88	39	110	36.5	1.057	1
7	137	90	41	0	32	0.391	0
0	123	72	0	0	36.3	0.258	1
1	106	76	0	0	37.5	0.197	0
6	190	92	0	0	35.5	0.278	1
9	f	74	31	0	44	0.403	1
9	89	62	0	0	22.5	e	0
10	101	76	48	180	d	0.171	0
2	122	70	27	b	36.8	0.34	0
c	121	72	23	112	26.2	0.245	0
1	126	60	a	0	30.1	0.349	1.